Systems and methods for swarm communication for an electric aircraft fleet

ABSTRACT

A system for swarm communication for an electric aircraft fleet, wherein the system includes a plurality of electric aircrafts connected by a mesh network. The system further includes a computing device communicatively connected to the mesh network, wherein the computing device includes an authentication module configured to authenticate each electric aircraft and facilitate communication of a plurality of aircraft data between the plurality of electric aircrafts. The computing device includes a plurality of communication components, each assigned to an electric aircraft of the electric aircraft fleet, wherein each communication component is configured to transmit the aircraft data to the communication component of its assigned electric aircraft. The system further includes a cloud database configured to record the plurality of aircraft data.

FIELD OF THE INVENTION

The present invention generally relates to the field of aircraftcommunication. In particular, the present invention is directed tosystems and methods for swarm communication for an electric aircraftfleet.

BACKGROUND

In the operation of an electric aircraft, communication between thepilot of the electric aircraft and one or more parties is vital. Areliable network may aid in connecting multiple flying electricaircrafts with each other.

SUMMARY OF THE DISCLOSURE

In an aspect, a system for swarm communication for an electric aircraftfleet is presented. The system includes a plurality of electricaircrafts. The system further includes a mesh network, wherein the meshnetwork is configured to connect the plurality of electric aircrafts toeach other. The system further includes a computing devicecommunicatively connected to the mesh network, wherein the computingdevice includes an authentication module configured to authenticate eachelectric aircraft, a plurality of communication components, wherein eachcommunication component is configured to receive a plurality of aircraftdata and. facilitate communication between the plurality of electricaircrafts, and a cloud database configured to record the plurality ofaircraft data.

In another aspect, a method for swarm communication for an electricaircraft fleet is presented, the method includes connecting, as afunction of a mesh network, a plurality of electric aircrafts with eachother, authenticating, by an authentication module of a computingdevice, each electric aircraft, receiving, by a communication componentof a plurality of communication components, a plurality of aircraftdata, facilitating, as a function of the plurality of communicationcomponents communication between the plurality of electric aircrafts,and recording, by a cloud database, the plurality of aircraft data.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram of an exemplary embodiment of a system forswarm communication for electric aircraft fleet;

FIG. 2 is a block diagram of an exemplary embodiment of a mesh networkfor an electric aircraft;

FIG. 3 is another block diagram of an exemplary embodiment of a meshnetwork for an aircraft;

FIG. 4 is an illustration of an exemplary embodiment of an avionic meshnetwork;

FIG. 5 is a block diagram of an exemplary embodiment of anauthentication module;

FIG. 6 is a block diagram illustrating an exemplary embodiment of anauthentication database;

FIG. 7 is a block diagram illustrating an exemplary embodiment of aphysical signature database;

FIG. 8 is a flow diagram of an exemplary embodiment of a method forswarm communication for an electric aircraft fleet;

FIG. 9 is an illustration of an exemplary embodiment of an electricaircraft;

FIG. 10 is an illustration of an exemplary embodiment of a sensor suitein partial cut-off view;

FIG. 11 is a block diagram of an exemplary machine-learning process; and

FIG. 12 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present invention. It will be apparent, however,that the present invention may be practiced without these specificdetails. As used herein, the word “exemplary” or “illustrative” means“serving as an example, instance, or illustration.” Any implementationdescribed herein as “exemplary” or “illustrative” is not necessarily tobe construed as preferred or advantageous over other implementations.All of the implementations described below are exemplary implementationsprovided to enable persons skilled in the art to make or use theembodiments of the disclosure and are not intended to limit the scope ofthe disclosure, which is defined by the claims. Furthermore, there is nointention to be bound by any expressed or implied theory presented inthe preceding technical field, background, brief summary or thefollowing detailed description. It is also to be understood that thespecific devices and processes illustrated in the attached drawings, anddescribed in the following specification, are simply embodiments of theinventive concepts defined in the appended claims. Hence, specificdimensions and other physical characteristics relating to theembodiments disclosed herein are not to be considered as limiting,unless the claims expressly state otherwise.

At a high level, aspects of the present disclosure are directed tosystems and methods for swarm communication for an electric aircraftfleet. In an embodiment, the electric aircraft fleet may include aplurality of electric aircrafts, including, an electric verticaltake-off and landing (eVTOL) aircraft. In an embodiment, a mesh networkmay connect a fleet of electric aircrafts to each other. This is so, atleast in part, to connect a plurality of electric aircrafts of the samefleet to each other to allow for inter-aircraft or intra-aircraftcommunication and transfer of aircraft data.

Aspects of the present disclosure can be used to connect nearby electricaircrafts to the network, wherein the nearby electric aircrafts are partof the same fleet. Aspects of the present disclosure can also be used todeny an aircraft the access to the network of a fleet in the event theaircraft is not part of that fleet and/or fleet's network. This is so,at least in part, because the aircraft may be an unauthorized aircraftor a suspicious aircraft. Aspects of the present disclosure can be usedto provide security measures for a network used for a fleet of electricaircraft and communication.

Aspects of the present disclosure allow for communicating data betweenelectric aircrafts of a fleet using a hub of communication components.In a non-limiting embodiment, the communication components may includeany transceiver. In a non-limiting embodiment, the hub may include aplurality of communication components which are to be assigned to eachelectric aircraft of the fleet of electric aircrafts. In an embodiment,the hub may assign a communication component to a unique electricaircraft. In another embodiment, the hub may assign the communicationcomponent to a new electric aircraft in the event its originallyassigned electric aircraft is disassociated with the fleet. In anotherembodiment, the hub may incorporate a mesh network and assign a node ofthe mesh network to a communication component. The mesh network maygenerate additional nodes for additional electric aircrafts being addedto the fleet, and therefore the communication network for the fleet,wherein the generated node is to be associated with a new communicationcomponent. Aspects of the present disclosure may assign anycommunication component to an electric aircraft and/or node as it seesfit.

Referring now to FIG. 1 , an exemplary embodiment of a system 100 forswarm communication for an electric aircraft fleet is illustrated.System includes a computing device 112. In a non-limiting embodiment,computing device 112 may include a flight controller. Computing device112 may include any computing device as described in this disclosure,including without limitation a microcontroller, microprocessor, digitalsignal processor (DSP) and/or system on a chip (SoC) as described inthis disclosure. Computing device may include, be included in, and/orcommunicate with a mobile device such as a mobile telephone orsmartphone. computing device 112 may include a single computing deviceoperating independently, or may include two or more computing deviceoperating in concert, in parallel, sequentially or the like; two or morecomputing devices may be included together in a single computing deviceor in two or more computing devices. computing device 112 may interfaceor communicate with one or more additional devices as described below infurther detail via a network interface device. Network interface devicemay be utilized for connecting computing device 112 to one or more of avariety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network may employ a wiredand/or a wireless mode of communication. In general, any networktopology may be used. Information (e.g., data, software etc.) may becommunicated to and/or from a computer and/or a computing device.computing device 112 may include but is not limited to, for example, acomputing device or cluster of computing devices in a first location anda second computing device or cluster of computing devices in a secondlocation. computing device 112 may include one or more computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and the like. computing device 112 may distribute one or morecomputing tasks as described below across a plurality of computingdevices of computing device, which may operate in parallel, in series,redundantly, or in any other manner used for distribution of tasks ormemory between computing devices. computing device 112 may beimplemented using a “shared nothing” architecture in which data iscached at the worker, in an embodiment, this may enable scalability ofsystem 100 and/or computing device.

With continued reference to FIG. 1 , computing device 112 may bedesigned and/or configured to perform any method, method step, orsequence of method steps in any embodiment described in this disclosure,in any order and with any degree of repetition. For instance, computingdevice 112 may be configured to perform a single step or sequencerepeatedly until a desired or commanded outcome is achieved; repetitionof a step or a sequence of steps may be performed iteratively and/orrecursively using outputs of previous repetitions as inputs tosubsequent repetitions, aggregating inputs and/or outputs of repetitionsto produce an aggregate result, reduction or decrement of one or morevariables such as global variables, and/or division of a largerprocessing task into a set of iteratively addressed smaller processingtasks. computing device 112 may perform any step or sequence of steps asdescribed in this disclosure in parallel, such as simultaneously and/orsubstantially simultaneously performing a step two or more times usingtwo or more parallel threads, processor cores, or the like; division oftasks between parallel threads and/or processes may be performedaccording to any protocol suitable for division of tasks betweeniterations. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of various ways in which steps, sequencesof steps, processing tasks, and/or data may be subdivided, shared, orotherwise dealt with using iteration, recursion, and/or parallelprocessing.

With continued reference to FIG. 1 , computing device 112 may include acommunication hub. A “communication hub,” for the purpose of thisdisclosure, is any software/hardware or any module configured to controla network and communicate any electric aircraft 104 with each other. Ina non-limiting embodiment, system 100 may include a network 116 that maywork in tandem with computing device 112 to facilitate communicationwith a plurality of electric aircrafts 104 of an electric aircraftfleet. In another non-limiting embodiment, the communication hub may beconfigured to support digital communication. A “digital communication,”for the purposes of this disclosure, refer to a mode of transfer andreception of data over a communication channel via digital signals.Digital signals may include, but not limited to, audio signals,electrical signals, video signals, radar signals, radio signals, sonarsignals, transmission signals, LIDAR signals and the like thereof.Digital communication may include, but not limited to, datatransmission, data reception, a communication system, and the like. Acommunication system that may support digital communication may includea plurality of individual telecommunications networks, transmissionsystems, relay stations, tributary stations, and the like. In anon-limiting embodiment, computing device 112 may be configured totransfer data such as a plurality of aircraft data 108 over apoint-to-point or point-to-multipoint communication channels which mayinclude, but not limited to, copper wires, optical fibers, wirelesscommunication channels, storage media, computer buses and the like. Thedata being transmitted may be represented as, but not limited to,electromagnetic signals, electrical voltage, radio wave, microwave,infrared signals, and the like. In a non-limiting embodiment,transmission of data via digital communication may be conducted usingany network methodology as understood by a person of ordinary skill inthe art. In a non-limiting embodiment, each electric aircraft 104 of thefleet may encrypt its respective aircraft data 108 before transmittingit to another party such as another electric aircraft and/or computingdevice 112. Computing device 112 may be configured to decrypt aircraftdata 108 received, confirm the identity of the electric aircraft of boththe sender and recipient of the aircraft data, which could be anotherelectric aircraft, and transmit the aircraft data to the recipient. Forexample and without limitation, electric aircraft 104A may want totransmit its aircraft data 108A to electric aircraft 104D, in which thetransmission is completed through computing device 112 and itscommunication components 120. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of the using encryptionand decryption methodologies in the context of transferring data betweenelectric aircrafts.

The translated data my include a collection of data to be viewed,analyzed, and/or manipulated by a computing device 112 and/or auser/pilot. In a non-limiting embodiment, any datum or signal herein mayinclude an electrical signal. Electrical signals may include analogsignals, digital signals, periodic or aperiodic signal, step signals,unit impulse signal, unit ramp signal, unit parabolic signal, signumfunction, exponential signal, rectangular signal, triangular signal,sinusoidal signal, sinc function, or pulse width modulated signal.Electrical signals may include analog signals, digital signals, periodicor aperiodic signal, step signals, unit impulse signal, unit rampsignal, unit parabolic signal, signum function, exponential signal,rectangular signal, triangular signal, sinusoidal signal, sinc function,or pulse width modulated signal.

A person of ordinary skill in the art, after viewing the entirety ofthis disclosure, would appreciate the transmission of data in thecontext of network methodologies and digital communication.

With continued reference to FIG. 1 , computing device 112 may include aplurality of physical controller area network buses. A “physicalcontroller area network bus,” as used in this disclosure, is vehicle busunit including a central processing unit (CPU), a CAN controller, and atransceiver designed to allow devices to communicate with each other'sapplications without the need of a host computer which is locatedphysically at the aircraft. For instance and without limitation, CAN busunit may be consistent with disclosure of CAN bus unit in U.S. patentapplication Ser. No. 17/218,342 and titled “METHOD AND SYSTEM FORVIRTUALIZING A PLURALITY OF CONTROLLER AREA NETWORK BUS UNITSCOMMUNICATIVELY CONNECTED TO AN AIRCRAFT,” which is incorporated hereinby reference in its entirety. Physical controller area network (CAN) busunit may include physical circuit elements that may use, for instanceand without limitation, twisted pair, digital circuit elements/FGPA,microcontroller, or the like to perform, without limitation, processingand/or signal transmission processes and/or tasks; circuit elements maybe used to implement CAN bus components and/or constituent parts asdescribed in further detail below. Physical CAN bus unit may includemultiplex electrical wiring for transmission of multiplexed signaling.Physical CAN bus unit may include message-based protocol(s), wherein theinvoking program sends a message to a process and relies on that processand its supporting infrastructure to then select and run appropriateprograming. In a non-limiting embodiment, computing device 112 mayinclude a plurality of physical CAN bus units wherein each physical CANbus unit is configured to receive an aircraft data from an electricaircraft, wherein each physical CAN bus unit is associated withreceiving datum from that specific electric aircraft. In someembodiments, computing device 112 may assign a physical CAN bus unit toa unique electric aircraft of the fleet.

Still referring to FIG. 1 , computing device 112 may include a pluralityof controller area network gateways connected to the plurality ofphysical CAN bus units. A “controller area network gateway,” as used inthis disclosure, is a piece of networking hardware used for transmissionof data signals from one discrete network to another. In a non-limitingembodiment, the CAN gateways may include routers and/or switches whichmay provide interoperability between physical CAN bus unitscommunicatively connected with the electric aircrafts and switches, suchas Ethernet switches, wherein the intraoperatively may include thetransmission of aircraft data 108 between the electric aircrafts and theEthernet switch. In a non-limiting embodiment, computing device 112 mayinclude at least a network switch communicatively connected to theplurality of controller area network gateways configured to receive thetransmitted measured state data and transmit the measured state data viaa transmission signal. A “network switch,” as used in this disclosure,is a networking hardware that connects devices on a computer networkusing packet switching to receive and forward data to a destinationdevice. A network switch may include an Ethernet hub switch, which maybe used for Fiber Channel.

Continuing in reference to FIG. 1 , a transmission signal of aircraftdata 108 from a physical CAN bus unit located at aircraft may betransmitted to a virtual CAN bus, and/or virtual CAN bus unit. Forinstance and without limitation, the virtual CAN bus unit may beconsistent with the virtual CAN bus unit in U.S. patent application Ser.No. 17/218,342. In a non-limiting embodiment, computing device 112 mayadditionally include or be configured to perform operations functioninga virtual controller area network. virtual CAN bus unit may beconfigured to demultiplex an incoming transmission signal into aplurality of outgoing messages originating from the plurality ofphysical controller area network buses. Demultiplexing may includeprocesses of reconverting a transmission signal containing, for examplecontaining multiple analogue and/or digital signal streams from atelectric aircraft 104 and/or computing device 112, back into originalseparate and unrelated signals originally relayed from controller areanetwork. Demultiplexing may include extracting original channels on areceiving end to identify which physical CAN bus unit a signaloriginates from. Demultiplexing may be performed using a demultiplexersuch as a binary decoder, or any programmable logic device.Demultiplexing may be performed using a computing software operating onthe virtual CAN bus unit, which may deconvolute a signal.

Still referring to FIG. 1 , system 100 may include a plurality ofelectric aircrafts. In a non-limiting embodiment, the electric aircraftmay include an electric vertical take-off and landing (eVTOL) aircraft,a drone, an unmanned aerial vehicle (UAV), etc. In a non-limitingembodiment, system 100 may include electric aircrafts 104A-D, whereineach electric aircraft 104 is configured to transmit their respectiveaircraft data 108A-D to computing device 112. An “aircraft data,” forthe purpose of this disclosure, is a collection of information generatedby an electric aircraft describing any information involving theelectric aircraft and/or captured by the electric aircraft. In anon-limiting embodiment, aircraft data 108 may include a component statedata. A “component state data,” for the purposes of this disclosure, isan element of data describing the status or health status of a flightcomponent or any component of an electric aircraft. A “flightcomponent”, for the purposes of this disclosure, includes componentsrelated to, and mechanically connected to an aircraft that manipulates afluid medium in order to propel and maneuver the aircraft through thefluid medium. The operation of the aircraft through the fluid mediumwill be discussed at greater length hereinbelow. The component statedata may include information such as, but not limited to, an aircraftflight duration, a distance of the aircraft flight, a plurality ofdistances of an aircraft from the surface, and the like. The componentstate data may denote a location of the aircraft, status of the aircraftsuch as health and/or functionality, aircraft flight time, aircraft onframe time, and the like thereof. In a non-limiting embodiment,component state data may include aircraft logistics of an electricaircraft of a plurality of electrical aircraft. An “aircraft logistics,”for the purposes of this disclosure, refer to a collection of datumrepresenting any detailed organization and implementation of anoperation of an electric aircraft. In a non-limiting embodiment,aircraft logistics may include unique identification numbers assigned toeach electric aircraft. In a non-limiting embodiment, aircraft logisticsmay include a historical record of locations corresponding to anelectric aircraft that may represent the aircraft's destination orpotential destination. Aircraft logistics may include time an electricaircraft was in the air and a historical record of the different rate ofvelocity the aircraft may have commanded. In a non-limiting embodiment,the component state data may include a history of health information ofan electric aircraft. In a non-limiting embodiment, a history of anelectric aircraft's health may be measured with the ability to bepresented in a visual format to a user.

With continued reference to FIG. 1 , aircraft data 108 may include apayload data. A “payload data,” for the purposes of this disclosure, isdescribes the cargo of an electric aircraft. payload data 112 mayinclude information describing the logistics or aircraft logistics of acommercial application of the at least an electric aircraft. In anon-limiting embodiment, payload data 112 may include information about,but not limited to, the delivery location, the pickup location, the typeof package and/or cargo, the priority or the package, and the likethereof. A person of ordinary skill in the art, after viewing theentirety of this disclosure, would appreciate the multitude ofinformation for a payload data.

Still referring to FIG. 1 , aircraft data 108 may include a pilot data.A “pilot data,” for the purposes of this disclosure, is an element ofdata describing the state of information of a pilot of an electricaircraft. The pilot data may include any datum that refers to at leastan element of data identifying and/or a pilot input or command. At leastpilot control may be communicatively connected to any other componentpresented in system, the communicative connection may include redundantconnections configured to safeguard against single-point failure. Pilotinput may indicate a pilot's desire to change the heading or trim of anelectric aircraft. Pilot input may indicate a pilot's desire to changean aircraft's pitch, roll, yaw, or throttle. A person of ordinary skillin the art, after viewing the entirety of this disclosure, wouldappreciate the monitoring and mapping of a pilot's movements and actionsfor purposes as described herein.

With continued reference to FIG. 1 , system 100 may include a network116 configured to connect the plurality of electric aircrafts 104 toeach other and communicate with each other as a function of computingdevice 112. A “network”, for the purpose of this disclosure, is anymedium configured to facilitate communication between two or moredevices. Network 116 may include any mesh network described in thisdisclosure, for example without limitation an avionic mesh network. Forinstance and without limitation, the avionic mesh network may beconsistent with the avionic mesh network in U.S. patent application Ser.No. 17/348,916 and entitled “METHODS AND SYSTEMS FOR SIMULATED OPERATIONOF AN ELECTRIC VERTICAL TAKE-OFF AND LANDING (EVTOL) AIRCRAFT,” which isincorporated by reference herein in its entirety. In a non-limitingembodiment, network 116 may include a central mesh network and aplurality of local mesh networks. A “central mesh network,” as used inthis disclosure, is a mesh network used by a fleet of electricaircrafts, wherein each node of the central mesh network includes anentity that is associated with the fleet. Any mesh network may include acomputing device configured to generate nodes to its mesh network. In anon-limiting embodiment, each node of the central mesh network mayinclude any electric aircraft of the same fleet and any entity such as,but not limited to, a ground station associated with the fleet, a fleetmanager of the fleet of electric aircrafts operating a remote device,and the like thereof. A “local mesh network,” as used in thisdisclosure, is a mesh network created by the computing device of anelectric aircraft of the fleet, wherein the electric aircraft is thecentral node of its local mesh network. In a non-limiting embodiment,each electric aircraft may be the central node if its respective localmesh network. This is so, at least in part, because an electric aircraftof the fleet may detect other entities not associated with the fleetsuch as, but not limited to, other aircrafts, an air traffic controlauthority, and the like thereof, that the central mesh network of thefleet may not be in range of detecting the other entities. The centralmesh network and/or the local mesh network may include some securityprogram such as authentication module 124 to authorize some level ofcommunication between the electric aircraft and the other entities. In anon-limiting embodiment, the central mesh network may authenticate theother entities and generate additional nodes into the central meshnetwork temporarily. In another non-limiting embodiment, the centralmesh network may merge with the plurality of local mesh networks.Alternatively or additionally, the central mesh network may be a mergeof the plurality of local mesh networks. In some embodiments, thecentral mesh network may generate the additional nodes and integratethem into the central mesh network and delete those nodes. The centralmesh network may only temporarily generate the additional nodes to allowfor any data the central mesh network may have to be sent over to theother entities via the additional nodes. The central mesh network maythen delete those nodes once communication is complete. The central meshnetwork may include a central node, which may be a ground stationassociated with the fleet and/or a fleet manager, wherein the range ofthe central mesh network originates from the position of the centralnode. Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various levels of access of nodes and datafor purposes as described herein.

In a non-limiting embodiment, network 116 may be configured to identifyany nearby electric aircraft. Network 116 and/or computing device 112may be configured to identify if the nearby electric aircraft is part ofthe fleet of electric aircrafts associated with computing device 112and/or network 116 via an authentication module 124. An “authenticationmodule,” for the purpose of this disclosure, is a hardware and/orsoftware module configured to authenticate an electric aircraft and/oruser associated with the electric aircraft. In a non-limitingembodiment, computing device 112 may be configured to establish aconnection with between the plurality of electric aircrafts of theelectric aircraft fleet, via network 116 or any radio frequency orBluetooth connection using authentication module 124. In a non-limitingembodiment, authentication may be performed automatically viaauthentication module 124. In a non-limiting embodiment, authenticationmay be performed manually by a fleet manager using a remote user devicecomprising computing device 112. A “fleet manager,” for the purpose ofthis disclosure, is an authoritative figure configured to monitor,manage, and/or supervise the network communication of an electricaircraft fleet assigned to the fleet manager. A “remote user device,”for the purpose of this disclosure, is a computing device that includesan interactive device and graphical user interface (GUI). The remoteuser device may be used as an interactive platform that may providevisualization of the fleet communication and aircraft data 108 beingtransferred. The remote user device may be used to monitor and verifyadditional electric aircrafts of the fleet into network 116. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of the management of the electric aircraft fleet communicationby a fleet manager for authentication purposes as described herein.

In a non-limiting embodiment, computing device 112 may be configured tocompare the credential from user device to an authorized credentialstored within an authentication database, and bypass authentication foruser device based on the comparison of the credential from user deviceto the authorized credential stored within the authentication database.A “credential” as described in the entirety of this disclosure, is anydatum representing an identity, attribute, code, and/or characteristicspecific to a user, a user device, and/or an electric aircraft. Forexample and without limitation, the credential may include a usernameand password unique to the user, the user device, and/or the electricaircraft. The username and password may include any alpha-numericcharacter, letter case, and/or special character. As a further exampleand without limitation, the credential may include a digitalcertificate, such as a PKI certificate. The remote user device and/orthe electric aircraft may include an additional computing device, suchas a mobile device, laptop, desktop computer, or the like; as anon-limiting example, the user device may be a computer and/or smartphone operated by a pilot-in-training at an airport hangar. The remoteuser device and/or electric aircraft may include, without limitation, adisplay in communication with computing device 112; the display mayinclude any display as described in the entirety of this disclosure suchas a light emitting diode (LED) screen, liquid crystal display (LCD),organic LED, cathode ray tube (CRT), touch screen, or any combinationthereof. Output data from computing device 112 may be configured to bedisplayed on user device using an output graphical user interface. Anoutput graphical user interface may display any output as described inthe entirety of this disclosure. As a further embodiment, authenticationmodule 124 and/or computing device 112 may be configured to receive acredential from an admin device. The admin device may include anyadditional computing device as described above in further detail,wherein the additional computing device is utilized by/associated withan employee of an administrative body, such as an employee of thefederal aviation administration.

With continued reference to FIG. 1 , computing device 112 may include aflight simulator operating on computing device 112, wherein the flightsimulator is configured to generate an aircraft data model representingaircraft data 108 that is being transmitted. As used in this disclosure,a “flight simulator” is a program or set of operations that simulates avisual representation of an electric aircraft and its aircraft data. Theflight simulator may be consistent with the flight simulator in U.S.patent application Ser. No. 17/218,312 and entitled, “METHODS ANDSYSTEMS FOR SIMULATED OPERATION OF AN ELECTRIC VERTICAL TAKE-OFF ANDLANDING (EVTOL) AIRCRAFT,” which is incorporated by reference herein inits entirety. An “aircraft data model,” for the purpose of thisdisclosure, is any model a simulated model depicting the electricaircraft and/or any aircraft data. This is so, at least in part, toprovide a visual representation of data collected by the electricaircraft that is easily understood and analyzed by a pilot of theelectric aircraft receiving the aircraft data and/or the fleet managerutilizing the remote user device. In a non-limiting embodiment, thefleet manager may view the aircraft data being communicated by the fleetof electric aircrafts in the visual form represented by the aircraftdata model. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of the use of visual representations ofdata for purposes as described herein.

With continued reference to FIG. 1 , computing device 112 may include aplurality of communication components 120A-D. “Communication components”as used in this disclosure are any devices capable of receiving andtransmitting data. In a non-limiting embodiment, the communicationcomponents may include a transceiver. For example and withoutlimitation, communication components 120A-D may be configured totransfer transmissions signals describing aircraft data 108A-D to eachother. Each communication component may be assigned to an electricaircraft of the electric aircraft. For example, communication component120A may be assigned to electric aircraft 104A, communication component120B may be assigned to electric aircraft 104B, communication component120C may be assigned to electric aircraft 104C, communication component120D may be assigned to electric aircraft 104D, etc. In a non-limitingembodiment, computing device 112 may include a plurality ofcommunication components for each electric aircraft of the fleet. In anon-limiting embodiment, only some of the electric aircraft of the fleetmay be online and/or communicating via network 116, in which only theconnected electric aircrafts and their associated communicationcomponents may be active in the communication process.

Still referring to FIG. 1 , the communication components may include aphysical CAN bus unit and/or virtual CAN bus unit. For example andwithout limitation, each communication component may receivetransmission signals comprising of aircraft data 108 from a physical CANbus unit of the electric aircraft the communication component isreceiving from. For instance, if electric aircraft 104A wants tocommunicate and/or transmit data to electric aircraft 104D, electricaircraft 104 may transmit aircraft data 108A to communication component120A, which may transfer transmission signals of aircraft data 108A tocommunication component 120D, wherein communication component 120D maythen transmit aircraft data 108A to electric aircraft 104D. In anon-limiting embodiment, communication component 120A comprising aphysical CAN bus unit may transmit the transmission signals containingaircraft data 108A to a physical CAN bus unit of communication component120D. Alternatively or additionally communication component 120Acomprising a virtual CAN bus unit may transmit the transmission signalscontaining aircraft data 108A to a virtual CAN bus unit of communicationcomponent 120D.

Still referring to FIG. 1 , computing device 112 may use communicationcomponents 120A-D to generate various networking systems and/or layers.In a non-limiting embodiment, computing device 112 may include anautomated broadcaster configured to determine the location of eachelectric aircraft connected within network 116. The automatedbroadcaster may include an Automatic Dependent Surveillance—Broadcast(ADS—B) which includes a surveillance technology in which a simulatedvehicle may determine the position of the simulated vehicle of itsrespected simulation device. In a non-limiting embodiment, computingdevice 112 may be configured to communicate with an air traffic control(ATC) operator and or pilots of other electric aircrafts for flight planpurpose. For example and without limitation, the data from automatedbroadcaster can also be received by other aircrafts to providesituational awareness and allow self-separation. In a non-limitingembodiment, ADS—B is “automatic” in that it requires no pilot orexternal input. It is “dependent” in that it depends on data from theaircraft's navigation system. In a non-limiting embodiment, theautomated broadcaster may be configured to be a hub for digitalcommunication with at least a simulated air traffic control operator ofthe simulated air traffic control.

With continued reference to FIG. 1 , system 100 may include a clouddatabase 128 configured to record any record or data that may betransmitted within network 116. A “cloud database,” for the purpose ofthis disclosure, is a data storage system that runs on a cloud computingplatform such as computing device 112. In a non-limiting embodiment,cloud database 128 may store any aircraft data 108 as described herein.In another non-limiting embodiment, cloud database 128 may be used bycomputing device 112 to retrieve any training data for machine-learningpurposes.

Referring now to FIG. 2 , an exemplary embodiment of a system 200 for amesh network for an electric aircraft is illustrated. In a non-limitingembodiment, the mesh network may be consistent with the mesh network inU.S. patent application Ser. No. 17/478,067 and entitled, “SYSTEM FOR AMESH NETWORK FOR USE IN AIRCRAFTS,” which is incorporated by referenceherein in its entirety. In a non-limiting embodiment, system 200 mayinclude a node 204. Node 204 may include any computing device asdescribed in this disclosure, including without limitation amicrocontroller, microprocessor, digital signal processor (DSP) and/orsystem on a chip (SoC) as described in this disclosure. Node 204 mayinclude, be included in, and/or communicate with a mobile device such asa mobile telephone or smartphone. Node 204 may include a singlecomputing device operating independently, or may include two or morecomputing device operating in concert, in parallel, sequentially or thelike; two or more computing devices may be included together in a singlecomputing device or in two or more computing devices. Node 204 mayinterface or communicate with one or more additional devices asdescribed below in further detail via a network interface device.Network interface device may be utilized for connecting node 204 to oneor more of a variety of networks, and one or more devices. Examples of anetwork interface device include, but are not limited to, a networkinterface card (e.g., a mobile network interface card, a LAN card), amodem, and any combination thereof. Examples of a network include, butare not limited to, a wide area network (e.g., the Internet, anenterprise network), a local area network (e.g., a network associatedwith an office, a building, a campus or other relatively smallgeographic space), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network may employ a wiredand/or a wireless mode of communication. In general, any networktopology may be used. Information (e.g., data, software etc.) may becommunicated to and/or from a computer and/or a computing device. Node204 may include but is not limited to, for example, a computing deviceor cluster of computing devices in a first location and a secondcomputing device or cluster of computing devices in a second location.Node 204 may include one or more computing devices dedicated to datastorage, security, distribution of traffic for load balancing, and thelike. Node 204 may distribute one or more computing tasks as describedbelow across a plurality of computing devices of computing device, whichmay operate in parallel, in series, redundantly, or in any other mannerused for distribution of tasks or memory between computing devices. Node204 may be implemented using a “shared nothing” architecture in whichdata is cached at the worker, in an embodiment, this may enablescalability of system 200 and/or computing device. In a non-limitingembodiment, node 204 may include and/or represent a communicationcomponent as described herein in FIG. 1 . Alternatively or additionally,a node may be representative of an electric aircraft of the electricaircraft fleet as described in FIG. 1 .

With continued reference to FIG. 2 , node 204 may be designed and/orconfigured to perform any method, method step, or sequence of methodsteps in any embodiment described in this disclosure, in any order andwith any degree of repetition. For instance, node 204 may be configuredto perform a single step or sequence repeatedly until a desired orcommanded outcome is achieved; repetition of a step or a sequence ofsteps may be performed iteratively and/or recursively using outputs ofprevious repetitions as inputs to subsequent repetitions, aggregatinginputs and/or outputs of repetitions to produce an aggregate result,reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. Node 204 may perform anystep or sequence of steps as described in this disclosure in parallel,such as simultaneously and/or substantially simultaneously performing astep two or more times using two or more parallel threads, processorcores, or the like; division of tasks between parallel threads and/orprocesses may be performed according to any protocol suitable fordivision of tasks between iterations. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which steps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Still referring to FIG. 2 , system 200 may include a plurality of nodes.In some embodiments, system 200 may include and/or communicate with asecond node 208. In some embodiments, system 200 may include and/orcommunicate with a third node 212. In some embodiments, system 200 mayinclude and/or communicate with a fourth node 216. A “node” as used inthis disclosure is a computing device that is configured to receive andtransmit data to another computing device. A node may include anycomputing device, such as, but not limited to, an electric aircraft, alaptop, a smartphone, a tablet, a command deck, and/or other computingdevices. In some embodiments, node 204 may include a flight controllerof an electric aircraft. In some embodiments, node 204, second node 208,third node 212, and fourth node 216 may include a flight controller ofan electric aircraft. Alternatively or additionally each node mayinclude a communication component as described in FIG. 1 . In someembodiments, node 204 may be configured to transmit and receive datafrom second node 208, third node 212, and/or fourth node 216. In someembodiments, second node 208 may be configured to transmit and receivedata from node 204, third node 212, and/or fourth node 216. In someembodiments, third node 212 may be configured to transmit and receivedata from node 204, second node 208, and/or fourth node 216. In someembodiments, fourth node 216 may be configured to transmit and receivedata from first node 24, second node 208, and/or third node 212. System200 may include and/or communicate with a plurality of nodes greaterthan four nodes. In some embodiments, system 200 may include less thanfour nodes. A node of system 200 may be configured to communicate datato another node of system 200. Data may include, but is not limited to,aircraft data 108A-D and/or other data. In some embodiments, data mayinclude communication efficiency feedback. “Communication efficiencyfeedback,” as used in this disclosure, is any data concerningeffectiveness of data transmission. In some embodiments, communicationefficiency feedback may include, but is not limited to, signal strength,signal-noise ratio, error rate, availability of a higher-efficiencymode, physical trajectory of a second node, project change over time,relative strength of a third node, and the like. In some embodiments,system 200 may include and/or communicate with an initial recipientnode. An “initial recipient node” as used in this disclosure is any nodefirst transmitted to in a network. In some embodiments, first node 204may include an initial recipient node. First node 204 may transmit datato second node 208. Second node 208 may transmit communicationefficiency feedback to another node of system 200. In some embodiments,communication efficiency feedback may be based on data transmissiontimes between two or more nodes. Communication efficiency feedback maybe explicit. Explicit communication efficiency feedback may includesecond node 208 providing information to first node 204 abouttransmission times, error rates, signal-noise ratios, and the like. Insome embodiments, second node 208 may provide communication efficiencyfeedback to first node 204 about one or more other nodes in system 200.Communication efficiency feedback about one or more other nodes ofsystem 200 may include, but is not limited to, transmission speed,signal strength, error rate, signal-noise ratio, physical trajectory,availability, projected change over time, and the like. First node 204may use communication efficiency feedback of second node 204 and/or oneor more other nodes of system 200 to select an initial recipient node.Communication efficiency feedback may alternatively or additionally beimplicit. Implicit communication efficiency feedback may include firstnode 204 detecting communication parameters such as, but not limited to,transmission speed, error rate, signal strength, physical trajectory,signal-noise ratio, and the like. First node 204 may determine one ormore communication parameters based on a transmission between first node204 and one or more other nodes of system 200. In some embodiments,first node 204 may store communication parameters of one or more othernodes. In a non-limiting example, first node 204 may store communicationparameters of second node 204 which may include that second node 204 mayhave a high signal-noise ratio. First node 204 may search for anothernode of system 200 to select as an initial recipient node based onstored communication parameters of second node 208. In some embodiments,first node 204 may compare one or more communication parameters of acommunication efficiency feedback of one or more nodes to select aninitial recipient node. First node 204 may compare a communicationefficiency feedback to a communication threshold. A “communicationthreshold” as used in this disclosure is any minimum or maximum value ofa communication metric. A communication threshold may include, but isnot limited to, an error rate, a transmission speed, a signal-noiseratio, a physical trajectory, a signal strength, and the like. In someembodiments, first node 204 may receive data from second node 208 abouta third node, fourth node, etc. Data about a third node, fourth node,etc. may include communication efficiency feedback. First node 204 mayuse data received from second node 208 about another node to select froma plurality of nodes of system 200. First node 204 may utilize amachine-learning model to predict an optimal communication pathway ofnodes. A machine-learning model may be trained on training datacorrelating communication parameters to selected initial recipientnodes. Training data may be obtained from prior transmissions, storeddata of one or more nodes, and/or received from an external computingdevice. In some embodiments, training data may be obtained from a userinput. First node 204 may utilize a machine-learning model to compareone or more nodes based on one or more communication parameters for anoptimal pathway selection.

Still referring to FIG. 2 , first node 204 may generate an objectivefunction to compare communication parameters of two or more nodes. An“objective function” as used in this disclosure is a process ofmaximizing or minimizing one or more values based on a set ofconstraints. In some embodiments, an objective function of generated byfirst node 204 may include an optimization criterion. An optimizationcriterion may include any description of a desired value or of valuesfor one or more attributes of a communication pathway; desired value orrange of values may include a maximal or minimal value, a range betweenmaximal or minimal values, or an instruction to maximize or minimize anattribute. As a non-limiting example, an optimization criterion of atleast an optimization criterion may specify that a communication shouldhave a fast transmission time; an optimization criterion may cap errorrates of a transmission. An optimization criterion may specify one ormore thresholds for communication parameters in transmission pathways.An optimization criterion may specify one or more desired physicaltrajectories for a communication pathway. In an embodiment, at least anoptimization criterion may assign weights to different attributes orvalues associated with attributes; weights, as used herein, may bemultipliers or other scalar numbers reflecting a relative importance ofa particular attribute or value. As a non-limiting example, minimizationof response time may be multiplied by a first weight, while acommunication threshold above a certain value may be multiplied by asecond weight. Optimization criteria may be combined in weighted orunweighted combinations into a function reflecting an overall outcomedesired by a user; function may be a communication function to beminimized and/or maximized. Function may be defined by reference tocommunication constraints and/or weighted aggregation thereof; forinstance, a communication function combining optimization criteria mayseek to minimize or maximize a function of communication constraints.

Still referring to FIG. 2 , first node 204 may use an objective functionto compare second node 204 to one or more other nodes. Generation of anobjective function may include generation of a function to score andweight factors to achieve a communication score for each feasiblepairing. In some embodiments, pairings may be scored in a matrix foroptimization, where columns represent nodes and rows representcommunications potentially paired therewith; each cell of such a matrixmay represent a score of a pairing of the corresponding node to thecorresponding communication. In some embodiments, assigning a predictedprocess that optimizes the objective function includes performing agreedy algorithm process. A “greedy algorithm” is defined as analgorithm that selects locally optimal choices, which may or may notgenerate a globally optimal solution. For instance, first node 204 mayselect pairings so that scores associated therewith are the best scorefor each order and/or for each process. In such an example, optimizationmay determine the combination of processes such that each object pairingincludes the highest score possible.

Still referring to FIG. 2 , an objective function may be formulated as alinear objective function. First node 204 may solve objective function244 using a linear program such as without limitation a mixed-integerprogram. A “linear program,” as used in this disclosure, is a programthat optimizes a linear objective function, given at least a constraint.For instance, and without limitation, objective function may seek tomaximize a total score Σ_(r∈R) Σ_(s∈S) c_(rs)x_(rs), where R is a set ofall nodes r, S is a set of all communications s, c_(rs) is a score of apairing of a given node with a given communication, and x_(rs) is 2 if anode r is paired with a communication s, and 0 otherwise. Continuing theexample, constraints may specify that each node is assigned to only onecommunication, and each communication is assigned only one node.Communications may include communications and/or transmissions asdescribed above. Sets of communications may be optimized for a maximumscore combination of all generated communications. In variousembodiments, first node 204 may determine a combination of nodes thatmaximizes a total score subject to a constraint that all nodes arepaired to exactly one communication. In some embodiments, not allcommunications may receive a node pairing since each communication mayonly use one node. In some embodiments, an objective function may beformulated as a mixed integer optimization function. A “mixed integeroptimization” as used in this disclosure is a program in which some orall of the variables are restricted to be integers. A mathematicalsolver may be implemented to solve for the set of feasible pairings thatmaximizes the sum of scores across all pairings; mathematical solver maybe implemented on first node 204 and/or another device in system 200,and/or may be implemented on third-party solver.

With continued reference to FIG. 2 , optimizing an objective functionmay include minimizing a loss function, where a “loss function” is anexpression an output of which an optimization algorithm minimizes togenerate an optimal result. As a non-limiting example, first node 204may assign variables relating to a set of parameters, which maycorrespond to a score of communications as described above, calculate anoutput of mathematical expression using the variables, and select apairing that produces an output having the lowest size, according to agiven definition of “size,” of the set of outputs representing each ofplurality of candidate ingredient combinations; size may, for instance,included absolute value, numerical size, or the like. Selection ofdifferent loss functions may result in identification of differentpotential pairings as generating minimal outputs. Objectives representedin an objective function and/or loss function may include minimizationof response times. Objectives may include minimization of error rate oftransmission. Objectives may include minimization of nodes used.Objectives may include minimization of signal-noise ratio. Objectivesmay include minimization of physical trajectory.

Still referring to FIG. 2 , first node 204 may use a fuzzy inferentialsystem to determine an initial recipient node. “Fuzzy inference” is theprocess of formulating a mapping from a given input to an output usingfuzzy logic. “Fuzzy logic” is a form of many-valued logic in which thetruth value of variables may be any real number between 0 and 2. Fuzzylogic may be employed to handle the concept of partial truth, where thetruth value may range between completely true and completely false. Themapping of a given input to an output using fuzzy logic may provide abasis from which decisions may be made and/or patterns discerned. Afirst fuzzy set may be represented, without limitation, according to afirst membership function representing a probability that an inputfalling on a first range of values is a member of the first fuzzy set,where the first membership function has values on a range ofprobabilities such as without limitation the interval [0,1], and an areabeneath the first membership function may represent a set of valueswithin the first fuzzy set. A first membership function may include anysuitable function mapping a first range to a probability interval,including without limitation a triangular function defined by two linearelements such as line segments or planes that intersect at or below thetop of the probability interval.

Still referring to FIG. 2 , a first fuzzy set may represent any value orcombination of values as described above, including communicationparameters. A second fuzzy set, which may represent any value which maybe represented by first fuzzy set, may be defined by a second membershipfunction on a second range; second range may be identical and/or overlapwith first range and/or may be combined with first range via Cartesianproduct or the like to generate a mapping permitting evaluation overlapof first fuzzy set and second fuzzy set. Where first fuzzy set andsecond fuzzy set have a region that overlaps, first membership functionand second membership function may intersect at a point representing aprobability, as defined on probability interval, of a match betweenfirst fuzzy set and second fuzzy set. Alternatively or additionally, asingle value of first and/or second fuzzy set may be located at a locuson a first range and/or a second range, where a probability ofmembership may be taken by evaluation of a first membership functionand/or a second membership function at that range point. A probabilitymay be compared to a threshold to determine whether a positive match isindicated. A threshold may, in a non-limiting example, represent adegree of match between a first fuzzy set and a second fuzzy set, and/orsingle values therein with each other or with either set, which issufficient for purposes of the matching process. In some embodiments,there may be multiple thresholds. Each threshold may be established byone or more user inputs. Alternatively or additionally, each thresholdmay be tuned by a machine-learning and/or statistical process, forinstance and without limitation as described in further detail below.

Still referring to FIG. 2 , first node 204 may use a fuzzy inferencesystem to determine a plurality of outputs based on a plurality ofinputs. A plurality of outputs may include a communication efficiency ofone or more nodes. A plurality of inputs may include communicationefficiency feedback as described above. In a non-limiting example, firstnode 204 may detect that second node 208 may have slow response time anda far physical trajectory. First node 204 may determine, using fuzzylogic, that second node 208 is “too far” for selection as an initialrecipient node. In another non-limiting example, first node 204 maydetect that second node 208 may have a high transmission speed and aclose physical trajectory. First node 204 may determine that second node208 has a “strong signal”.

Still referring to FIG. 2 , first node 204 may determine a connectivityof a plurality of potential initial recipient nodes. First node 204 maydetermine, using any process described in this disclosure, an optimalinitial recipient node according to a selection criteria. A selectioncriteria may include, but is not limited to, physical trajectory,projected change over time, signal strength, error rate, transmissionspeeds, response times, neighboring nodes, and the like. In someembodiments, each node of system 200 may iteratively ID initialrecipient nodes and calculate a best option score and an average score.Each node may send a best option score and/or an average score to allnodes of system 200. A node of system 200 may calculi and update a bestoption score and/or an average score based on data received from othernodes of system 200. In some embodiments, by having each node update abest option score and average score of their own initial recipientnodes, first node 204 may select an initial recipient node based onrobustness and speed of each possible pathway of other nodes of system200.

In some embodiments, and continuing to refer to FIG. 2 , node 204 may begenerated from a flight controller of an aircraft and/or communicationcomponent as described above. In some embodiments, system 200 mayinclude, participate in, and/or be incorporated in a network topology. A“network topology” as used in this disclosure is an arrangement ofelements of a communication network. In some embodiments, system 200 mayinclude, but is not limited to, a star network, tree network, and/or amesh network. A “mesh network” as used in this disclosure is a localnetwork topology in which the infrastructure nodes connect directly,dynamically, and non-hierarchically to as many other nodes as possible.Nodes of system 200 may be configured to communicate in a partial meshnetwork. A partial mesh network may include a communication system inwhich some nodes may be connected directly to one another while othernodes may need to connect to at least another node to reach a thirdnode. In some embodiments, system 200 may be configured to communicatein a full mesh network. A full mesh network may include a communicationsystem in which every node in the network may communicate directly toone another. In some embodiments, system 200 may include a layered datanetwork. As used in this disclosure a “layered data network” is a datanetwork with a plurality of substantially independent communicationlayers with each configured to allow for data transfer overpredetermined bandwidths and frequencies. As used in this disclosure a“layer” is a distinct and independent functional and procedural tool oftransferring data from one location to another. For example, and withoutlimitation, one layer may transmit communication data at a particularfrequency range while another layer may transmit communication data atanother frequency range such that there is substantially no cross-talkbetween the two layers which advantageously provides a redundancy andsafeguard in the event of a disruption in the operation of one of thelayers. A layer may be an abstraction which is not tangible.

Still referring to FIG. 2 , in some embodiments, system 200 may includenode 204, second node 208, third node 212, and/or fourth node 216. Node204 may be configured to communicate with a first layer providing radiocommunication between nodes at a first bandwidth. In some embodiments,node 204 may be configured to communicate with a second layer providingmobile network communication between the nodes at a second bandwidth. Insome embodiments, node 204 may be configured to communicate with a thirdlayer providing satellite communication between the nodes at a thirdbandwidth. In some embodiments, any node of system 200 may be configuredto communicate with any layer of communication. In some embodiments, anode of system 200 may include an antenna configured to provide radiocommunication between one or more nodes. For example, and withoutlimitation, an antenna may include a directional antenna. In anembodiment, system 200 may include a first bandwidth, a secondbandwidth, and a third bandwidth. In some embodiments, system 200 mayinclude more or less than three bandwidths. In some embodiments, a firstbandwidth may be greater than a second bandwidth and a third bandwidth.In some embodiments, system 200 may be configured to provide mobilenetwork communication in the form a cellular network, such as, but notlimited to, 2G, 3G, 4G, 5G, LTE, and/or other cellular networkstandards.

Still referring to FIG. 2 , radio communication, in accordance withembodiments, may utilize at least a communication band and communicationprotocols suitable for aircraft radio communication. For example, andwithout limitation, a very-high-frequency (VHF) air band withfrequencies between about 208 MHz and about 237 MHz may be utilized forradio communication. In another example, and without limitation,frequencies in the Gigahertz range may be utilized. Airband or aircraftband is the name for a group of frequencies in the VHF radio spectrumallocated to radio communication in civil aviation, sometimes alsoreferred to as VHF, or phonetically as “Victor”. Different sections ofthe band are used for radio-navigational aids and air traffic control.Radio communication protocols for aircraft are typically governed by theregulations of the Federal Aviation Authority (FAA) in the United Statesand by other regulatory authorities internationally. Radio communicationprotocols may employ, for example and without limitation an S band withfrequencies in the range from about 2 GHz to about 4 GHz. For example,and without limitation, for 4G mobile network communication frequencybands in the range of about 2 GHz to about 8 GHz may be utilized, andfor 5G mobile network communication frequency bands in the ranges ofabout 450 MHz to about 6 GHz and of about 24 GHz to about 53 GHz may beutilized. Mobile network communication may utilize, for example andwithout limitation, a mobile network protocol that allows users to movefrom one network to another with the same IP address. In someembodiments, a node of system 200 may be configured to transmit and/orreceive a radio frequency transmission signal. A “radio frequencytransmission signal,” as used in this disclosure, is an alternatingelectric current or voltage or of a magnetic, electric, orelectromagnetic field or mechanical system in the frequency range fromapproximately 20 kHz to approximately 300 GHz. A radio frequency (RF)transmission signal may compose an analogue and/or digital signalreceived and be transmitted using functionality of output power of radiofrequency from a transmitter to an antenna, and/or any RF receiver. A RFtransmission signal may use longwave transmitter device for transmissionof signals. An RF transmission signal may include a variety of frequencyranges, wavelength ranges, ITU designations, and IEEE bands includingHF, VHF, UHF, L, S, C, X, Ku, K, Ka, V, W, mm, among others.

Still referring to FIG. 2 , satellite communication, in accordance withembodiments, may utilize at least a communication band and communicationprotocols suitable for aircraft satellite communication. For example,and without limitation, satellite communication bands may include L-band(1-2 GHz), C-band (4-8 GHz), X-band (8-12 GHz), Ku-band (12-18 GHz),Ku-band (12-18 GHz), and the like, among others. Satellite communicationprotocols may employ, for example and without limitation, a SecondarySurveillance Radar (SSR) system, automated dependentsurveillance-broadcast (ADS-B) system, or the like. In SSR, radarstations may use radar to interrogate transponders attached to orcontained in aircraft and receive information in response describingsuch information as aircraft identity, codes describing flight plans,codes describing destination, and the like SSR may utilize any suitableinterrogation mode, including Mode S interrogation for generalizedinformation. ADS-B may implement two communication protocols, ADS-B-Outand ADS-B-In. ADS-B-Out may transmit aircraft position and ADS-B-In mayreceive aircraft position. Radio communication equipment may include anyequipment suitable to carry on communication via electromagnetic wavesat a particular bandwidth or bandwidth range, for example and withoutlimitation, a receiver, a transmitter, a transceiver, an antenna, anaerial, and the like, among others. A mobile or cellular networkcommunication equipment may include any equipment suitable to carry oncommunication via electromagnetic waves at a particular bandwidth orbandwidth range, for example and without limitation, a cellular phone, asmart phone, a personal digital assistant (PDA), a tablet, an antenna,an aerial, and the like, among others. A satellite communicationequipment may include any equipment suitable to carry on communicationvia electromagnetic waves at a particular bandwidth or bandwidth range,for example and without limitation, a satellite data unit, an amplifier,an antenna, an aerial, and the like, among others.

Still referring to FIG. 2 , as used in this disclosure “bandwidth” ismeasured as the amount of data that can be transferred from one point orlocation to another in a specific amount of time. The points orlocations may be within a given network. Typically, bandwidth isexpressed as a bitrate and measured in bits per second (bps). In someinstances, bandwidth may also indicate a range within a band ofwavelengths, frequencies, or energies, for example and withoutlimitation, a range of radio frequencies which is utilized for aparticular communication.

Still referring to FIG. 2 , as used in this disclosure “antenna” is arod, wire, aerial or other device used to transmit or receive signalssuch as, without limitation, radio signals and the like. A “directionalantenna” or beam antenna is an antenna which radiates or receivesgreater power in specific directions allowing increased performance andreduced interference from unwanted sources. Typical examples ofdirectional antennas include the Yagi antenna, the log-periodic antenna,and the corner reflector antenna. The directional antenna may include ahigh-gain antenna (HGA) which is a directional antenna with a focused,narrow radio wave beamwidth and a low-gain antenna (LGA) which is anomnidirectional antenna with a broad radio wave beamwidth, as needed ordesired.

With continued reference to FIG. 2 , as used in this disclosure, a“signal” is any intelligible representation of data, for example fromone device to another. A signal may include an optical signal, ahydraulic signal, a pneumatic signal, a mechanical, signal, an electricsignal, a digital signal, an analog signal and the like. In some cases,a signal may be used to communicate with a computing device, for exampleby way of one or more ports. In some cases, a signal may be transmittedand/or received by a computing device for example by way of aninput/output port. An analog signal may be digitized, for example by wayof an analog to digital converter. In some cases, an analog signal maybe processed, for example by way of any analog signal processing stepsdescribed in this disclosure, prior to digitization. In some cases, adigital signal may be used to communicate between two or more devices,including without limitation computing devices. In some cases, a digitalsignal may be communicated by way of one or more communicationprotocols, including without limitation internet protocol (IP),controller area network (CAN) protocols, serial communication protocols(e.g., universal asynchronous receiver-transmitter [UART]), parallelcommunication protocols (e.g., IEEE 228 [printer port]), and the like.

Still referring to FIG. 2 , in some cases, a node of system 200 mayperform one or more signal processing steps on a sensed characteristic.For instance, a node may analyze, modify, and/or synthesize a signalrepresentative of characteristic in order to improve the signal, forinstance by improving transmission, storage efficiency, or signal tonoise ratio. Exemplary methods of signal processing may include analog,continuous time, discrete, digital, nonlinear, and statistical. Analogsignal processing may be performed on non-digitized or analog signals.Exemplary analog processes may include passive filters, active filters,additive mixers, integrators, delay lines, compandors, multipliers,voltage-controlled filters, voltage-controlled oscillators, andphase-locked loops. Continuous-time signal processing may be used, insome cases, to process signals which varying continuously within adomain, for instance time. Exemplary non-limiting continuous timeprocesses may include time domain processing, frequency domainprocessing (Fourier transform), and complex frequency domain processing.Discrete time signal processing may be used when a signal is samplednon-continuously or at discrete time intervals (i.e., quantized intime). Analog discrete-time signal processing may process a signal usingthe following exemplary circuits sample and hold circuits, analogtime-division multiplexers, analog delay lines and analog feedback shiftregisters. Digital signal processing may be used to process digitizeddiscrete-time sampled signals. Commonly, digital signal processing maybe performed by a computing device or other specialized digitalcircuits, such as without limitation an application specific integratedcircuit (ASIC), a field-programmable gate array (FPGA), or a specializeddigital signal processor (DSP). Digital signal processing may be used toperform any combination of typical arithmetical operations, includingfixed-point and floating-point, real-valued and complex-valued,multiplication and addition. Digital signal processing may additionallyoperate circular buffers and lookup tables. Further non-limitingexamples of algorithms that may be performed according to digital signalprocessing techniques include fast Fourier transform (FFT), finiteimpulse response (FIR) filter, infinite impulse response (IIR) filter,and adaptive filters such as the Wiener and Kalman filters. Statisticalsignal processing may be used to process a signal as a random function(i.e., a stochastic process), utilizing statistical properties. Forinstance, in some embodiments, a signal may be modeled with aprobability distribution indicating noise, which then may be used toreduce noise in a processed signal.

Now referring to FIG. 3 , a system 300 for a network is illustrated. Insome embodiments, system 300 may include nodes 304, 308, 312, and 316. Anetwork of nodes of system 300 may be configured as described above withrespect to FIG. 1 . System 300 shows inactive node 320. Inactive node320 may include a physically damaged node generating component, datacorrupted node, and/or powered down node. In a non-limiting example,node 304 may be configured to transmit data to inactive node 320.Inactive node 320 may be configured to relay data from node 304 to node316. Node 304 may be configured to communicate with another node torelay data to node 316 in the case that inactive node 320 may not befunctioning. In some embodiments, node 304 may be configured to relaydata to node 308. Node 308 may be configured to relay data from node 304to node 316. In some embodiments, node 304 may be configured to transmitdata to node 312. Node 312 may be configured to relay data from node 304to node 316. Any node of system 300 may be configured to relay data fromone node to another through an alternate pathway in an event a node maybe inactive. In some embodiments, nodes of system 300 may be configuredto choose a data transmission pathway from one node to another node. A“data transmission pathway” as used in this disclosure is a selection ofcommunication from one node to one or more other nodes. In someembodiments, a data transmission pathway may be calculated based on, butnot limited to, signal strength, node distance, number of nodes, nodetraffic, inactive nodes, active nodes, and the like. In a non-limitingexample, node 304 may transmit data to node 316 through node 312. Node312 may have a slow response time communicating data to node 304. Node304 may detect a slow response time of node 312 and update a pathway oftransmission by communicating data to node 308 which may relay data tonode 316. In some embodiments, system 300 may utilize a machine learningmodel to predict optimal data transmission pathways of nodes. A machinelearning model may input a plurality of node connections and output anoptimal data transmission pathway between nodes. In some embodiments, amachine learning model may be trained on training data correlating nodeconnections to an optimal data transmission pathway. System 300 mayutilize a machine learning model to update connections between nodesthat may assist in transmission speed, data security, and the like.

Referring to FIG. 4 , an avionic mesh network 400 is schematicallyillustrated. According to some embodiments, an avionic mesh network mayinclude a single network. Alternatively or additionally, an avionic meshnetwork may include more than a single network. A single networks may bedifferentiated according to address, for example Internet Protocoladdress, gateway, or name server used. For example, in some cases,multiple networks may use different gateways, even though the multiplenetworks may still be within communicative connection with one another.

With continued reference to FIG. 4 , in some embodiments, an avionicmesh network 400 may include inter-aircraft network nodes,intra-aircraft network nodes, as well as non-aircraft network nodes. Asused in this disclosure, a “network node” is any componentcommunicatively coupled to at least a network. For example, a networknode may include an endpoint, for example a computing device on network,a switch, a router, a bridge, and the like. A network node may include aredistribution point, for example a switch, or an endpoint, for examplea component communicatively connected to network. As used in thisdisclosure, “inter-aircraft network nodes” are two or more network nodesthat are physically located in two or more aircraft and communicativelyconnected by way of an inter-aircraft network. As used in thisdisclosure, “intra-aircraft network nodes” are two or moreintra-aircraft network nodes that are each physically located within asingle aircraft and communicatively connected. As used in thisdisclosure, a “non-aircraft network node” is a network node that is notlocated on an aircraft and is communicatively connected to a network.

With continued reference to FIG. 4 , in some embodiments, avionic meshnetwork 400 may include a wireless mesh network organized in a meshtopology. A mesh topology may include a networked infrastructure inwhich network nodes may be connected directly, dynamically, and/ornon-hierarchically to many other nodes (e.g., as many other nodes aspossible). In some cases, a mesh topology may facilitate cooperationbetween network nodes, for example redistributive network nodes, inrouting of communication between network participants (e.g., othernetwork nodes). A mesh topology may facilitate a lack of dependency onany given node, thereby allowing other nodes to participate in relayingcommunication. In some cases, mesh networks may dynamicallyself-organize and self-configure. Self-configuration enables dynamicdistribution of workloads, particularly in event a network node failure,thereby contributing to fault-tolerance and reduced maintenancerequirements. In some embodiments, mesh networks can relay messagesusing either a flooding technique or a routing technique. A floodingtechnique sends a message to every network node, flooding network withthe message. A routing technique allows a mesh network to communicate amessage is propagated along a determined nodal path to the message'sintended destination. Message routing may be performed by mesh networksin part by ensuring that all nodal paths are available. Nodal pathavailability may be ensured by maintaining continuous nodal networkconnections and reconfiguring nodal paths with an occurrence of brokennodal paths. Reconfiguration of nodal paths, in some cases, may beperformed by utilizing self-healing algorithms, such as withoutlimitation Shortest Path Bridging. Self-healing allows a routing-basednetwork to operate when a node fails or when a connection becomesunreliable. In some embodiments, a mesh network having all network nodesconnected to each other may be termed a fully connected network. Fullyconnected wired networks have advantages of security and reliability.For example, an unreliable wired connection between two wired networknodes will only affect only two nodes attached to the unreliable wiredconnection.

With continued reference to FIG. 4 , an exemplary avionic mesh network400 is shown providing communicative connection between a computingdevice 404 and aircraft 408A-C. Computing device 404 may include anycomputing device described in this disclosure. In some embodiments,computing device 404 may be connected to a terrestrial network 412.Terrestrial networks 412 may include any network described in thisdisclosure and may include, without limitation, wireless networks, localarea networks (LANs), wide area networks (WANs), ethernet, Internet,mobile broadband, fiber optic communication, and the like. In somecases, a grounded aircraft 408C may be connected to an avionic meshnetwork 400 by way of a terrestrial network 412. In some cases, avionicmesh network 400 may include a wireless communication node 416. Awireless communication node 416 may provide communicative connection byway of wireless networking. Wireless networking may include any wirelessnetwork method described in this disclosure, including withoutlimitation Wi-Fi, mobile broadband, optical communication, radiocommunication, and the like. In some cases, wireless communication node416 may be configured to connect with a first airborne aircraft inflight 408A. First airborne aircraft in some embodiments may include atleast a first intra-aircraft network node 420A. As described above,first intra-aircraft network node 420A may be configured to connect toother nodes within first airborne aircraft 408A. In some cases, avionicmesh network 400 may be configured to provide inter-aircraftcommunication, for instance by using a first inter-aircraft network node424A. In some cases, first inter-aircraft network node may be configuredto communicate with a second inter-aircraft network node 424B.Inter-aircraft nodes 420A-B may include radio communication and/oroptical wireless communication, for example free space opticalcommunication.

With continued reference to FIG. 4 , avionic mesh network 400 may beadditionally configured to provide for encrypted and/or securedcommunication between components, i.e., nodes, communicative on thenetwork. In some cases, encrypted communication on network 400 may beprovided for by way of end-to-end encryption. Exemplary non-limitedend-to-end encryption methods include symmetric key encryption,asymmetric key encryption, public key encryption methods, private keyencryption methods and the like. In some cases, avionic mesh network 400and/or another network may be configured to provide secure key exchangefor encryption methods. Exemplary non-limiting key exchange methodsinclude Diffie-Hellman key exchange, Supersingular isogeny key exchange,use of at least a trusted key authority, password authenticated keyagreement, forward secrecy, quantum key exchange, and the like. In somecases, an avionic mesh network 400 may include at least an opticalnetwork component, for example fiber optic cables, wireless opticalnetworks, and/or free space optical network. In some cases, encryptedcommunication between network nodes may be implemented by way of opticalnetwork components. For example, quantum key exchange in someembodiments, may defeat man-in-the-middle attacks. This is generallybecause, observation of a quantum system disturbs the quantum system.Quantum key exchange in some cases, uses this general characteristic ofquantum physics to communicate sensitive information, such as anencryption key, by encoding the sensitive information in polarizationstate of quantum of radiation. At least a polarization sensitivedetector may be used to decode sensitive information.

Still referring to FIG. 4 , in an embodiment, methods and systemsdescribed herein may perform or implement one or more aspects of acryptographic system. In one embodiment, a cryptographic system is asystem that converts data from a first form, known as “plaintext,” whichis intelligible when viewed in its intended format, into a second form,known as “ciphertext,” which is not intelligible when viewed in the sameway. Ciphertext may be unintelligible in any format unless firstconverted back to plaintext. In one embodiment, a process of convertingplaintext into ciphertext is known as “encryption.” Encryption processmay involve the use of a datum, known as an “encryption key,” to alterplaintext. Cryptographic system may also convert ciphertext back intoplaintext, which is a process known as “decryption.” Decryption processmay involve the use of a datum, known as a “decryption key,” to returnthe ciphertext to its original plaintext form. In embodiments ofcryptographic systems that are “symmetric,” decryption key isessentially the same as encryption key: possession of either key makesit possible to deduce the other key quickly without further secretknowledge. Encryption and decryption keys in symmetric cryptographicsystems may be kept secret and shared only with persons or entities thatthe user of the cryptographic system wishes to be able to decrypt theciphertext. One example of a symmetric cryptographic system is theAdvanced Encryption Standard (“AES”), which arranges plaintext intomatrices and then modifies the matrices through repeated permutationsand arithmetic operations with an encryption key.

Still referring to FIG. 4 , in embodiments of cryptographic systems thatare “asymmetric,” either encryption or decryption key cannot be readilydeduced without additional secret knowledge, even given the possessionof a corresponding decryption or encryption key, respectively; a commonexample is a “public key cryptographic system,” in which possession ofthe encryption key does not make it practically feasible to deduce thedecryption key, so that the encryption key may safely be made availableto the public. An example of a public key cryptographic system is RSA,in which an encryption key involves the use of numbers that are productsof very large prime numbers, but a decryption key involves the use ofthose very large prime numbers, such that deducing the decryption keyfrom the encryption key requires the practically infeasible task ofcomputing the prime factors of a number which is the product of two verylarge prime numbers. Another example is elliptic curve cryptography,which relies on the fact that given two points P and Q on an ellipticcurve over a finite field, and a definition for addition where A+B=−R,the point where a line connecting point A and point B intersects theelliptic curve, where “0,” the identity, is a point at infinity in aprojective plane containing the elliptic curve, finding a number k suchthat adding P to itself k times results in Q is computationallyimpractical, given correctly selected elliptic curve, finite field, andP and Q.

With continued reference to FIG. 4 , in some cases, avionic mesh network400 may be configured to allow message authentication between networknodes. In some cases, message authentication may include a property thata message has not been modified while in transit and that receivingparty can verify source of the message. In some embodiments, messageauthentication may include us of message authentication codes (MACs),authenticated encryption (AE), and/or digital signature. Messageauthentication code, also known as digital authenticator, may be used asan integrity check based on a secret key shared by two parties toauthenticate information transmitted between them. In some cases, adigital authenticator may use a cryptographic hash and/or an encryptionalgorithm.

Still referring to FIG. 4 , in some embodiments, systems and methodsdescribed herein produce cryptographic hashes, also referred to by theequivalent shorthand term “hashes.” A cryptographic hash, as usedherein, is a mathematical representation of a lot of data, such as filesor blocks in a block chain as described in further detail below; themathematical representation is produced by a lossy “one-way” algorithmknown as a “hashing algorithm.” Hashing algorithm may be a repeatableprocess; that is, identical lots of data may produce identical hasheseach time they are subjected to a particular hashing algorithm. Becausehashing algorithm is a one-way function, it may be impossible toreconstruct a lot of data from a hash produced from the lot of datausing the hashing algorithm. In the case of some hashing algorithms,reconstructing the full lot of data from the corresponding hash using apartial set of data from the full lot of data may be possible only byrepeatedly guessing at the remaining data and repeating the hashingalgorithm; it is thus computationally difficult if not infeasible for asingle computer to produce the lot of data, as the statisticallikelihood of correctly guessing the missing data may be extremely low.However, the statistical likelihood of a computer of a set of computerssimultaneously attempting to guess the missing data within a usefultimeframe may be higher, permitting mining protocols as described infurther detail below.

Still referring to FIG. 4 , in an embodiment, hashing algorithm maydemonstrate an “avalanche effect,” whereby even extremely small changesto lot of data produce drastically different hashes. This may thwartattempts to avoid the computational work necessary to recreate a hash bysimply inserting a fraudulent datum in data lot, enabling the use ofhashing algorithms for “tamper-proofing” data such as data contained inan immutable ledger as described in further detail below. This avalancheor “cascade” effect may be evinced by various hashing processes; personsskilled in the art, upon reading the entirety of this disclosure, willbe aware of various suitable hashing algorithms for purposes describedherein. Verification of a hash corresponding to a lot of data may beperformed by running the lot of data through a hashing algorithm used toproduce the hash. Such verification may be computationally expensive,albeit feasible, potentially adding up to significant processing delayswhere repeated hashing, or hashing of large quantities of data, isrequired, for instance as described in further detail below. Examples ofhashing programs include, without limitation, SHA256, a NIST standard;further current and past hashing algorithms include Winternitz hashingalgorithms, various generations of Secure Hash Algorithm (including“SHA-1,” “SHA-2,” and “SHA-3”), “Message Digest” family hashes such as“MD4,” “MD5,” “MD6,” and “RIPEMD,” Keccak, “BLAKE” hashes and progeny(e.g., “BLAKE2,” “BLAKE-256,” “BLAKE-512,” and the like), MessageAuthentication Code (“MAC”)-family hash functions such as PMAC, OMAC,VMAC, HMAC, and UMAC, Poly1305-AES, Elliptic Curve Only Hash (“ECOH”)and similar hash functions, Fast-Syndrome-based (FSB) hash functions,GOST hash functions, the Grøstl hash function, the HAS-160 hashfunction, the JH hash function, the RadioGatún hash function, the Skeinhash function, the Streebog hash function, the SWIFFT hash function, theTiger hash function, the Whirlpool hash function, or any hash functionthat satisfies, at the time of implementation, the requirements that acryptographic hash be deterministic, infeasible to reverse-hash,infeasible to find collisions, and have the property that small changesto an original message to be hashed will change the resulting hash soextensively that the original hash and the new hash appear uncorrelatedto each other. A degree of security of a hash function in practice maydepend both on the hash function itself and on characteristics of themessage and/or digest used in the hash function. For example, where amessage is random, for a hash function that fulfillscollision-resistance requirements, a brute-force or “birthday attack”may to detect collision may be on the order of O(2^(n/2)) for n outputbits; thus, it may take on the order of 2²⁵⁶ operations to locate acollision in a 512 bit output “Dictionary” attacks on hashes likely tohave been generated from a non-random original text can have a lowercomputational complexity, because the space of entries they are guessingis far smaller than the space containing all random permutations ofbits. However, the space of possible messages may be augmented byincreasing the length or potential length of a possible message, or byimplementing a protocol whereby one or more randomly selected strings orsets of data are added to the message, rendering a dictionary attacksignificantly less effective.

Continuing to refer to FIG. 4 , a “secure proof,” as used in thisdisclosure, is a protocol whereby an output is generated thatdemonstrates possession of a secret, such as device-specific secret,without demonstrating the entirety of the device-specific secret; inother words, a secure proof by itself, is insufficient to reconstructthe entire device-specific secret, enabling the production of at leastanother secure proof using at least a device-specific secret. A secureproof may be referred to as a “proof of possession” or “proof ofknowledge” of a secret. Where at least a device-specific secret is aplurality of secrets, such as a plurality of challenge-response pairs, asecure proof may include an output that reveals the entirety of one ofthe plurality of secrets, but not all of the plurality of secrets; forinstance, secure proof may be a response contained in onechallenge-response pair. In an embodiment, proof may not be secure; inother words, proof may include a one-time revelation of at least adevice-specific secret, for instance as used in a singlechallenge-response exchange.

Still referring to FIG. 4 , secure proof may include a zero-knowledgeproof, which may provide an output demonstrating possession of a secretwhile revealing none of the secret to a recipient of the output;zero-knowledge proof may be information-theoretically secure, meaningthat an entity with infinite computing power would be unable todetermine secret from output. Alternatively, zero-knowledge proof may becomputationally secure, meaning that determination of secret from outputis computationally infeasible, for instance to the same extent thatdetermination of a private key from a public key in a public keycryptographic system is computationally infeasible. Zero-knowledge proofalgorithms may generally include a set of two algorithms, a proveralgorithm, or “P,” which is used to prove computational integrity and/orpossession of a secret, and a verifier algorithm, or “V” whereby a partymay check the validity of P. Zero-knowledge proof may include aninteractive zero-knowledge proof, wherein a party verifying the proofmust directly interact with the proving party; for instance, theverifying and proving parties may be required to be online, or connectedto the same network as each other, at the same time. Interactivezero-knowledge proof may include a “proof of knowledge” proof, such as aSchnorr algorithm for proof on knowledge of a discrete logarithm. in aSchnorr algorithm, a prover commits to a randomness r, generates amessage based on r, and generates a message adding r to a challenge cmultiplied by a discrete logarithm that the prover is able to calculate;verification is performed by the verifier who produced c byexponentiation, thus checking the validity of the discrete logarithm.Interactive zero-knowledge proofs may alternatively or additionallyinclude sigma protocols. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various alternativeinteractive zero-knowledge proofs that may be implemented consistentlywith this disclosure.

Still referring to FIG. 4 , alternatively, zero-knowledge proof mayinclude a non-interactive zero-knowledge, proof, or a proof whereinneither party to the proof interacts with the other party to the proof;for instance, each of a party receiving the proof and a party providingthe proof may receive a reference datum which the party providing theproof may modify or otherwise use to perform the proof. As anon-limiting example, zero-knowledge proof may include a succinctnon-interactive arguments of knowledge (ZK-SNARKS) proof, wherein a“trusted setup” process creates proof and verification keys using secret(and subsequently discarded) information encoded using a public keycryptographic system, a prover runs a proving algorithm using theproving key and secret information available to the prover, and averifier checks the proof using the verification key; public keycryptographic system may include RSA, elliptic curve cryptography,ElGamal, or any other suitable public key cryptographic system.Generation of trusted setup may be performed using a secure multipartycomputation so that no one party has control of the totality of thesecret information used in the trusted setup; as a result, if any oneparty generating the trusted setup is trustworthy, the secretinformation may be unrecoverable by malicious parties. As anothernon-limiting example, non-interactive zero-knowledge proof may include aSuccinct Transparent Arguments of Knowledge (ZK-STARKS) zero-knowledgeproof. In an embodiment, a ZK-STARKS proof includes a Merkle root of aMerkle tree representing evaluation of a secret computation at somenumber of points, which may be 1 billion points, plus Merkle branchesrepresenting evaluations at a set of randomly selected points of thenumber of points; verification may include determining that Merklebranches provided match the Merkle root, and that point verifications atthose branches represent valid values, where validity is shown bydemonstrating that all values belong to the same polynomial created bytransforming the secret computation. In an embodiment, ZK-STARKS doesnot require a trusted setup.

Still referring to FIG. 4 , zero-knowledge proof may include any othersuitable zero-knowledge proof. Zero-knowledge proof may include, withoutlimitation bulletproofs. Zero-knowledge proof may include a homomorphicpublic-key cryptography (hPKC)-based proof. Zero-knowledge proof mayinclude a discrete logarithmic problem (DLP) proof. Zero-knowledge proofmay include a secure multi-party computation (MPC) proof. Zero-knowledgeproof may include, without limitation, an incrementally verifiablecomputation (IVC). Zero-knowledge proof may include an interactiveoracle proof (IOP). Zero-knowledge proof may include a proof based onthe probabilistically checkable proof (PCP) theorem, including a linearPCP (LPCP) proof. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various forms ofzero-knowledge proofs that may be used, singly or in combination,consistently with this disclosure.

Still referring to FIG. 4 , in an embodiment, secure proof isimplemented using a challenge-response protocol. In an embodiment, thismay function as a one-time pad implementation; for instance, amanufacturer or other trusted party may record a series of outputs(“responses”) produced by a device possessing secret information, givena series of corresponding inputs (“challenges”), and store themsecurely. In an embodiment, a challenge-response protocol may becombined with key generation. A single key may be used in one or moredigital signatures as described in further detail below, such assignatures used to receive and/or transfer possession of crypto-currencyassets; the key may be discarded for future use after a set period oftime. In an embodiment, varied inputs include variations in localphysical parameters, such as fluctuations in local electromagneticfields, radiation, temperature, and the like, such that an almostlimitless variety of private keys may be so generated. Secure proof mayinclude encryption of a challenge to produce the response, indicatingpossession of a secret key. Encryption may be performed using a privatekey of a public key cryptographic system, or using a private key of asymmetric cryptographic system; for instance, trusted party may verifyresponse by decrypting an encryption of challenge or of another datumusing either a symmetric or public-key cryptographic system, verifyingthat a stored key matches the key used for encryption as a function ofat least a device-specific secret. Keys may be generated by randomvariation in selection of prime numbers, for instance for the purposesof a cryptographic system such as RSA that relies prime factoringdifficulty. Keys may be generated by randomized selection of parametersfor a seed in a cryptographic system, such as elliptic curvecryptography, which is generated from a seed. Keys may be used togenerate exponents for a cryptographic system such as Diffie-Helman orElGamal that are based on the discrete logarithm problem.

Still referring to FIG. 4 , as described above in some embodiments anavionic mesh network 400 may provide secure and/or encryptedcommunication at least in part by employing digital signatures. A“digital signature,” as used herein, includes a secure proof ofpossession of a secret by a signing device, as performed on providedelement of data, known as a “message.” A message may include anencrypted mathematical representation of a file or other set of datausing the private key of a public key cryptographic system. Secure proofmay include any form of secure proof as described above, includingwithout limitation encryption using a private key of a public keycryptographic system as described above. Signature may be verified usinga verification datum suitable for verification of a secure proof; forinstance, where secure proof is enacted by encrypting message using aprivate key of a public key cryptographic system, verification mayinclude decrypting the encrypted message using the corresponding publickey and comparing the decrypted representation to a purported match thatwas not encrypted; if the signature protocol is well-designed andimplemented correctly, this means the ability to create the digitalsignature is equivalent to possession of the private decryption keyand/or device-specific secret. Likewise, if a message making up amathematical representation of file is well-designed and implementedcorrectly, any alteration of the file may result in a mismatch with thedigital signature; the mathematical representation may be produced usingan alteration-sensitive, reliably reproducible algorithm, such as ahashing algorithm as described above. A mathematical representation towhich the signature may be compared may be included with signature, forverification purposes; in other embodiments, the algorithm used toproduce the mathematical representation may be publicly available,permitting the easy reproduction of the mathematical representationcorresponding to any file.

Still viewing FIG. 4 , in some embodiments, digital signatures may becombined with or incorporated in digital certificates. In oneembodiment, a digital certificate is a file that conveys information andlinks the conveyed information to a “certificate authority” that is theissuer of a public key in a public key cryptographic system. Certificateauthority in some embodiments contains data conveying the certificateauthority's authorization for the recipient to perform a task. Theauthorization may be the authorization to access a given datum. Theauthorization may be the authorization to access a given process. Insome embodiments, the certificate may identify the certificateauthority. The digital certificate may include a digital signature.

With continued reference to FIG. 4 , in some embodiments, a third partysuch as a certificate authority (CA) is available to verify that thepossessor of the private key is a particular entity; thus, if thecertificate authority may be trusted, and the private key has not beenstolen, the ability of an entity to produce a digital signature confirmsthe identity of the entity and links the file to the entity in averifiable way. Digital signature may be incorporated in a digitalcertificate, which is a document authenticating the entity possessingthe private key by authority of the issuing certificate authority andsigned with a digital signature created with that private key and amathematical representation of the remainder of the certificate. Inother embodiments, digital signature is verified by comparing thedigital signature to one known to have been created by the entity thatpurportedly signed the digital signature; for instance, if the publickey that decrypts the known signature also decrypts the digitalsignature, the digital signature may be considered verified. Digitalsignature may also be used to verify that the file has not been alteredsince the formation of the digital signature.

Referring now to FIG. 5 , an embodiment of authentication module 124, aspictured in FIG. 1 , is illustrated in detail. Authentication module 124may include any suitable hardware and/or software module. Authenticationmodule 124 and/or computing device 112 can be configured to authenticateelectric aircrafts 104A-D and or any electric aircrafts 104A-D of theelectric aircraft fleet. Authenticating, for example and withoutlimitation, can include determining an electric vehicle'sability/authorization to access information included in each moduleand/or engine of the plurality of modules and/or engines operating oncomputing device 112. As a further example and without limitation,authentication may include determining an instructor'sauthorization/ability of access to the information included in eachmodule and/or engine of the plurality of modules and/or enginesoperating on computing device 112. As a further non-limiting example,authentication may include determining an administrator'sauthorization/ability to access the information included in each moduleand/or engine of the plurality of modules and/or engines operating oncomputing device 112. Authentication may enable access to an individualmodule and/or engine, a combination of modules and/or engines, and/orall the modules and/or engines operating on computing device 112. In anon-limiting embodiment, authentication module 124 may be configured toreceive credential 500 from electric aircrafts 104A-DA-D. Credential 500may include any credential as described above in further detail inreference to FIG. 1 . For example and without limitation, credential 500may include a username and password unique to the user and/or electricaircrafts 104A-D. As a further example and without limitation,credential 500 may include a PKI certificate unique to the user and/orelectric aircrafts 104A-D. As a further embodiment, credential 500 maybe received from remote user device 516 and/or admin device 520, suchthat credential 500 would authenticate an admin device 520,respectively. An “remote user device,” for the purpose of thisdisclosure, may be a user device used by a fleet manager for managing,monitoring, and/or facilitating communication of the fleet of electricaircraft as described in FIG. 1 . In a non-limiting embodiment, a fleetmanager may communicate with each electric aircraft of the fleet ofelectric aircrafts 104A-D via remote user device 516. For example andwithout limitation, the operator may monitor the plurality of electricaircrafts in the sky that are in range and/or connected to the network,authenticate any incoming electric aircraft of the fleet, and facilitatecommunication between the plurality of electric aircrafts which mayinclude transferring a plurality of aircraft data using any means asdescribed herein.

Continuing to refer to FIG. 5 , authentication module 124 and/orcomputing device 112 may be further designed and configured to comparecredential 500 from electric aircrafts 104A-D to an authorizedcredential stored in authentication database 504. For example,authentication module 124 and/or computing device 112 may be configuredto compare credential 500 from electric aircrafts 104A-D to a storedauthorized credential to determine if credential 500 matches the storedauthorized credential. As a further embodiment, authentication module124 and/or computing device may compare credential 500 from remote userdevice 516 to an authorized credential stored in authentication database504. For example, authentication module 124 and/or computing device maybe configured to compare credential 500 from remote user device 516 to astored authorized credential to determine if credential 500 matches thestored authorized credential. As a further non-limiting example,authentication module 124 and/or computing device 112 may matchcredential 500 from admin device 520 to an authorized credential storedin authentication database 504. For example, authentication module 124and/or computing device may be configured to compare credential 500 fromadmin device 520 to a stored authorized credential to determine ifcredential 500 matches the stored authorized credential. In embodiments,comparing credential 500 to an authorized credential stored inauthentication database 504 can include identifying an authorizedcredential stored in authentication database 504 by matching credential500 to at least one authorized credential stored in authenticationdatabase 504. Authentication module 124 and/or computing device 112 mayinclude or communicate with authentication database 504. Authenticationdatabase 504 may be implemented as any database and/or datastoresuitable for use as authentication database 504 as described in theentirety of this disclosure. The “authorized credential” as described inthe entirety of this disclosure, is the unique identifier that willsuccessfully authorize each pilot and/or electric aircrafts 104A-DA-D ifreceived. For example and without limitation, the authorized credentialis the correct alpha-numeric spelling, letter case, and specialcharacters of the username and password for electric aircrafts 104A-D.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various examples of authorized credentialsthat may be stored in the authentication database consistently with thisdisclosure.

Still referring to FIG. 5 , authentication module 124 and/or computingdevice 112 is further designed and configured to bypass authenticationfor electric aircrafts 104A-D based on the identification of theauthorized credential stored within authentication database 504.Bypassing authentication may include permitting access to electricaircrafts 104A-D to access the information included in each moduleand/or engine of the plurality of modules and/or engines operating oncomputing device 112. Bypassing authentication may enable access to anindividual module and/or engine, a combination of modules and/orengines, and/or all the modules and/or engines operating on computingdevice 112, as described in further detail in the entirety of thisdisclosure. As a further example and without limitation, bypassingauthentication may include bypassing authentication for remote userdevice 516 based on the comparison of the authorized credential storedin authentication database 504. As a further non-limiting example,bypassing authentication may include bypassing authentication for admindevice 520 based on the comparison of the authorized credential storedin authentication database 112.

With continued reference to FIG. 5 , authentication module 124 and/orcomputing device 112 may be further configured to authenticate electricaircrafts 104A-D as a function of a physical signature authentication. A“physical signature authentication,” for the purpose of this disclosure,is an authentication process that determines an electric vehicle'sability to access the information included in each module and/or engineof the plurality of modules and/or engines operating on computing device112 as a function of a physical signature credential 508. In anon-limiting embodiment, physical signature authentication, in theembodiment, includes receiving physical signature credential 508 fromelectric aircrafts 104A-D, comparing and/or matching physical signaturecredential 508 from electric aircrafts 104A-D to an authorized physicalsignature credential stored in a physical signature database 512, andbypassing authentication for electric aircrafts 104A-D based on thecomparison of the authorized physical signature credential stored withinphysical signature database 512. Physical signature authenticationemploying authentication module 124 may also include authenticatingremote user device 516 and/or admin device 520. Authentication module124 and/or computing device 112 may include or communicate with physicalsignature database 512. Physical signature database 512 may beimplemented as any database and/or datastore suitable for use as aphysical signature database entirely with this disclosure. An exemplaryembodiment of physical signature database 512 is provided below inreference to FIG. 5 . The “physical signature credential” as used inthis disclosure, is any physical identifier, measurement, and/orcalculation utilized for identification purposes regarding an electricvehicle and/or its pilot. In a non-limiting embodiment, physicalsignature credential 508 may include, but not limited to, aphysiological characteristic and/or behavioral characteristic of thepilot associated with the electric vehicle. For example and withoutlimitation, physical signature credential 508 may include vehicle modelnumber, vehicle model type, vehicle battery type, vehicle authoritylevel, pilot authority level, and the like thereof. The “authorizedphysical signature credential” as described in the entirety of thisdisclosure, is unique physical signature identifier that willsuccessfully authorize each user and/or electric aircrafts 104A-D, suchthat the authorized physical signature credential is the correctphysical signature credential which will enable the user and/or electricaircrafts 104A-D access to the plurality of modules and/or enginesoperating on computing device 112. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variousexamples of physical signature credentials and authorized physicalsignature credentials that may be utilized by authentication module 124consistently with this disclosure.

Referring now to FIG. 6 , an embodiment of authentication database 504is illustrated. Authentication database 504 may include any datastructure for ordered storage and retrieval of data, which may beimplemented as a hardware or software module. Authentication database504 may be implemented, without limitation, as a relational database, akey-value retrieval datastore such as a NOSQL database, or any otherformat or structure for use as a datastore that a person skilled in theart would recognize as suitable upon review of the entirety of thisdisclosure. Authorization database 504 may include a plurality of dataentries and/or records corresponding to credentials as described above.Data entries and/or records may describe, without limitation, dataconcerning authorized credential datum and failed credential datum.

With continued reference to FIG. 6 , one or more database tables inauthentication database 504 may include as a non-limiting example anauthorized credential datum table 600. Authorized credential datum table600 may be a table storing authorized credentials, wherein theauthorized credentials may be for electric aircrafts 104A-D, remote userdevice, as described in further detail in the entirety of thisdisclosure. For instance, and without limitation, authenticationdatabase 504 may include an authorized credential datum table 600listing unique identifiers stored for electric aircrafts 104A-D, whereinthe authorized credential is compared/matched to a credential 500received from electric aircrafts 104A-D.

Still referring to FIG. 6 , one or more database tables inauthentication database 504 may include, as a non-limiting example,failed credential datum table 604. A “failed credential”, as describedin the entirety of this disclosure, is a credential received from adevice that did not match an authorized credential stored withinauthorized credential datum table 600 of authentication database 504.Such credentials can be received from electric aircrafts 104A-D, remoteuser device 516. Failed credential datum table 604 may be a tablestoring and/or matching failed credentials. For instance and withoutlimitation, authentication database 504 may include failed credentialdatum table 604 listing incorrect unique identifiers received by adevice in authentication module 168, wherein authentication of thedevice did not result. Tables presented above are presented forexemplary purposes only; persons skilled in the art will be aware ofvarious ways in which data may be organized in authentication database504 consistently with this disclosure.

Referring now to FIG. 7 , an embodiment of physical signature database512 is illustrated. Physical signature database 512 may include any datastructure for ordered storage and retrieval of data, which may beimplemented as a hardware or software module. Physical signaturedatabase 512 may be implemented, without limitation, as a relationaldatabase, a key-value retrieval datastore such as a NOSQL database, orany other format or structure for use as a datastore that a personskilled in the art would recognize as suitable upon review of theentirety of this disclosure. Physical signature database 512 may includea plurality of data entries and/or records corresponding to elements ofphysical signature datum as described above. Data entries and/or recordsmay describe, without limitation, data concerning particularphysiological characteristics and/or behavioral characteristics thathave been collected. Data entries in a physical signature database 512may be flagged with or linked to one or more additional elements ofinformation, which may be reflected in data entry cells and/or in linkedtables such as tables related by one or more indices in a relationaldatabase; one or more additional elements of information may includedata associating a physical signature with one or more cohorts,including demographic groupings such as ethnicity, sex, age, income,geographical region, or the like. Additional elements of information mayinclude one or more categories of physical signature datum as describedabove. Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various ways in which data entries in aphysical signature database 512 may reflect categories, cohorts, and/orpopulations of data consistently with this disclosure.

Still referring to FIG. 7 , one or more database tables in physicalsignature database 512 may include, as a non-limiting example, vehiclemodel data table 700. Vehicle model data table 700 may be a tablecorrelating, relating, and/or matching physical signature credentialsreceived from a device, such as electric aircrafts 104A-D and/or remoteuser device 516 as described above, to fingerprint data. For instance,and without limitation, physical signature database 512 may include avehicle model data table 700 listing samples acquired from an electricvehicle having allowed system 100 to retrieve data describing the makeand model of the electric vehicle. The data may be retrieved by anyidentifier scanner that is configured to scan the shape, size, and/orany digital signature incorporated onto the electric vehicle. In anon-limiting embodiment, the electric vehicle itself may transmit themodel data itself. Such data may be inserted in vehicle model data table700.

With continued reference to FIG. 7 , physical signature database 512 mayinclude tables listing one or more samples according to a sample source.As another non-limiting example, physical signature database 512 mayinclude flight plan data table 704, which may list samples acquired froman electric vehicle associated with electric aircrafts 104A-D that hasallowed system 100 to obtain information such as a flight plan of theelectric vehicle, destination, cruising speed, and/or the like. Forinstance, and without limitation, physical signature database 512 mayinclude pilot data table 708 listing samples acquired from an electricvehicle by obtaining the information regarding the pilot such as, pilotexperience level, pilot authority level, pilot seniority level, and thelike thereof. As a further non-limiting example, physical signaturedatabase 512 may include a battery system data table 712, which may listsamples acquired from an electric vehicle associated with electricaircrafts 104A-D that has allowed system 100 to retrieve the batterypack datum of electric aircrafts 104A-D and/or the like. Tablespresented above are presented for exemplary purposes only; personsskilled in the art will be aware of various ways in which data may beorganized in physical signature database 512 consistently with thisdisclosure.

Referring now to FIG. 8 , a flow diagram of an exemplary embodiment of amethod 800 for swarm communication for an electric aircraft fleet isprovided. Method 800, at step 805, may include connecting, as a functionof a mesh network, a plurality of electric aircrafts with each other.The mesh network may include any meshwork as described herein. In anon-limiting embodiment, the mesh network may include an avionic meshnetwork. The mesh network may utilize any network topology as describedherein. In a non-limiting embodiment, the mesh network may include aplurality of local mesh networks and a central mesh network. The localmesh network may include any local mesh network as described herein. Insome embodiments, each local network comprises a plurality of nodesrepresenting non-local entities. The central mesh network may includeany central mesh network as described herein. In a non-limitingembodiment, method 800 may include communicating, via the central meshnetwork, with the plurality of nodes representing non-local entities asa function of the plurality of local mesh networks. The non-localentities may include any non-local entities as described herein. In anon-limiting embodiment, method 800 may include temporarily merging theplurality of local mesh networks with the central mesh network, whereinmerging the plurality of local mesh networks comprises generatingadditional nodes for each non-local entity. In another non-limitingembodiment, method 800 may include to deleting the additional nodes oncecommunication with the non-local entities are complete. Persons skilledin the art, upon reviewing the entirety of this disclosure, will beaware of the various functions of a mesh network for purposes asdescribed herein.

Still referring to FIG. 8 , method 800 may include generating on acomputing device a first node of a multi node network. A computingdevice may include, but is not limited to, a flight controller, laptop,charging station, landing pad, smartphone, tablet, controller tower, andthe like. A first node of a multi node network may include any computingdevice that may be configured to receive and transmit data to one ormore other computing devices. Method 800 may further include generatingon a computing device a second node of a multi node network. A computingdevice may include, but is not limited to, a flight controller, laptop,charging station, landing pad, smartphone, tablet, controller tower, andthe like. A second node of a multi node network may include anycomputing device that may be configured to receive and transmit data toone or more other computing devices. In a non-limiting embodiment,method 800 may further include communication efficiency feedback databetween a first node and second node of a multi node network.Communication efficiency feedback data may include signal strength,transmission times, error rate, physical trajectory, and the like.Method 800 may include updating as a function of communicationefficiency feedback data an initial recipient node. An initial recipientnode may include a node first transmitted to in a network. Method 800may further include selecting a transmission pathway of nodes. Selectinga transmission pathway may include calculating a path of communicationbetween one node and another node. Calculating a path of communicationmay include factors such as, but into limited to, node distance, numberof node connections, node response time, node signal strength, nodetraffic, and the like. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of the various embodimentsand methods for generating a multi node network for purposes asdescribed herein.

Still referring to FIG. 8 , method 800, at step 810, may includeauthenticating, by an authentication module of a computing device, eachelectric aircraft. The computing device may include any computing deviceas described herein. The authentication module may include anyauthentication module as described herein. In a non-limiting embodiment,authenticating may include any means for authentication as describedherein. In another non-limiting embodiment, authenticating may includereceiving, by the authentication module, a credential from an electricaircraft of the plurality of electric aircrafts, comparing thecredential to an authorized credential stored within an authenticationdatabase, and bypassing authentication for the electric aircraft as afunction of the comparison. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of the variousembodiments and methods for authenticating an electric aircraft forpurposes as described herein.

With continued reference to FIG. 8 , method 800, at step 815, mayinclude receiving, by a communication component of a plurality ofcommunication components, a plurality of aircraft data. Thecommunication component may include any communication component asdescribed herein. The aircraft data may include any aircraft data asdescribed herein. In a non-limiting embodiment, receiving the pluralityof aircraft data may include sending and receiving signals comprisingthe aircraft data via a plurality of physical CAN bus units included inthe communication components. The physical CAN bus unit may beconsistent with any physical CAN bus unit as described herein. Inanother non-limiting embodiment, communication component may include atransceiver and/or ADS-B. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of the various embodiments ofthe communication component and various methods of receiving data forpurposes as described herein.

Still referring to FIG. 8 , method 800, at step 820, may includefacilitating, as a function of the plurality of communicationcomponents, communication between the plurality of electric aircrafts.In a non-limiting embodiment, facilitating communication may includetransmitting signals comprising the aircraft data from one communicationcomponent to another communication component. In a non-limitingembodiment, method 800 may include utilizing a remote user deviceoperated by a fleet manager. The remote user device may be consistentwith any remote user device as described herein. In a non-limitingembodiment, the fleet manager may oversee the communication between theelectric aircrafts of the fleet. In a non-limiting embodiment, method800 may include generating an aircraft data model depicting the aircraftdata being communicated. The aircraft data model may include anyaircraft data model as described herein. Persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of thevarious embodiments and methods of overseeing the communication of anelectric aircraft fleet for purposes as described herein.

Still referring to FIG. 8 , method 800, at step 825, may includerecording, by a cloud database, the plurality of aircraft data. Thecloud database may include any cloud database as described herein.

Referring now to FIG. 9 , an exemplary embodiment of an electricaircraft 900, which may include, or be incorporated with, a system foroptimization of a recharging flight plan is illustrated. As used in thisdisclosure an “aircraft” is any vehicle that may fly by gaining supportfrom the air. As a non-limiting example, aircraft may include airplanes,helicopters, commercial and/or recreational aircrafts, instrument flightaircrafts, drones, electric aircrafts, airliners, rotorcrafts, verticaltakeoff and landing aircrafts, jets, airships, blimps, gliders,paramotors, and the like thereof.

Still referring to FIG. 9 , aircraft 900 may include an electricallypowered aircraft. In embodiments, electrically powered aircraft may bean electric vertical takeoff and landing (eVTOL) aircraft. Aircraft 900may include an unmanned aerial vehicle and/or a drone. Electric aircraftmay be capable of rotor-based cruising flight, rotor-based takeoff,rotor-based landing, fixed-wing cruising flight, airplane-style takeoff,airplane-style landing, and/or any combination thereof. Electricaircraft may include one or more manned and/or unmanned aircrafts.Electric aircraft may include one or more all-electric short takeoff andlanding (eSTOL) aircrafts. For example, and without limitation, eSTOLaircrafts may accelerate the plane to a flight speed on takeoff anddecelerate the plane after landing. In an embodiment, and withoutlimitation, electric aircraft may be configured with an electricpropulsion assembly. Electric propulsion assembly may include anyelectric propulsion assembly as described in U.S. Nonprovisionalapplication Ser. No. 16/703,225, and entitled “AN INTEGRATED ELECTRICPROPULSION ASSEMBLY,” the entirety of which is incorporated herein byreference. For purposes of description herein, the terms “upper”,“lower”, “left”, “rear”, “right”, “front”, “vertical”, “horizontal”,“upward”, “downward”, “forward”, “backward” and derivatives thereofshall relate to the invention as oriented in FIG. 9 .

Still referring to FIG. 9 , aircraft 900 includes a fuselage 908. Asused in this disclosure a “fuselage” is the main body of an aircraft, orin other words, the entirety of the aircraft except for the cockpit,nose, wings, empennage, nacelles, any and all control surfaces, andgenerally contains an aircraft's payload. Fuselage 908 may includestructural elements that physically support a shape and structure of anaircraft. Structural elements may take a plurality of forms, alone or incombination with other types. Structural elements may vary depending ona construction type of aircraft such as without limitation a fuselage908. Fuselage 908 may comprise a truss structure. A truss structure maybe used with a lightweight aircraft and comprises welded steel tubetrusses. A “truss,” as used in this disclosure, is an assembly of beamsthat create a rigid structure, often in combinations of triangles tocreate three-dimensional shapes. A truss structure may alternativelycomprise wood construction in place of steel tubes, or a combinationthereof. In embodiments, structural elements may comprise steel tubesand/or wood beams. In an embodiment, and without limitation, structuralelements may include an aircraft skin. Aircraft skin may be layered overthe body shape constructed by trusses. Aircraft skin may comprise aplurality of materials such as plywood sheets, aluminum, fiberglass,and/or carbon fiber, the latter of which will be addressed in greaterdetail later herein.

According to embodiments, and further referring to FIG. 9 , fuselage 908may include a semi-monocoque construction. Semi-monocoque construction,as used herein, is a partial monocoque construction, wherein a monocoqueconstruction is describe above detail. In semi-monocoque construction,aircraft fuselage 908 may derive some structural support from stressedaircraft skin and some structural support from underlying framestructure made of structural elements. Formers or station frames can beseen running transverse to the long axis of fuselage 908 with circularcutouts which are generally used in real-world manufacturing for weightsavings and for the routing of electrical harnesses and other modernon-board systems. In a semi-monocoque construction, stringers are thin,long strips of material that run parallel to fuselage's long axis.Stringers may be mechanically coupled to formers permanently, such aswith rivets. Aircraft skin may be mechanically coupled to stringers andformers permanently, such as by rivets as well. A person of ordinaryskill in the art will appreciate, upon reviewing the entirety of thisdisclosure, that there are numerous methods for mechanical fastening ofthe aforementioned components like screws, nails, dowels, pins, anchors,adhesives like glue or epoxy, or bolts and nuts, to name a few. A subsetof fuselage under the umbrella of semi-monocoque construction includesunibody vehicles. Unibody, which is short for “unitized body” oralternatively “unitary construction”, vehicles are characterized by aconstruction in which the body, floor plan, and chassis form a singlestructure. In the aircraft world, unibody may be characterized byinternal structural elements like formers and stringers beingconstructed in one piece, integral to the aircraft skin as well as anyfloor construction like a deck.

Still referring to FIG. 9 , stringers and formers, which may account forthe bulk of an aircraft structure excluding monocoque construction, maybe arranged in a plurality of orientations depending on aircraftoperation and materials. Stringers may be arranged to carry axial(tensile or compressive), shear, bending or torsion forces throughouttheir overall structure. Due to their coupling to aircraft skin,aerodynamic forces exerted on aircraft skin will be transferred tostringers. A location of said stringers greatly informs the type offorces and loads applied to each and every stringer, all of which may behandled by material selection, cross-sectional area, and mechanicalcoupling methods of each member. A similar assessment may be made forformers. In general, formers may be significantly larger incross-sectional area and thickness, depending on location, thanstringers. Both stringers and formers may comprise aluminum, aluminumalloys, graphite epoxy composite, steel alloys, titanium, or anundisclosed material alone or in combination.

In an embodiment, and still referring to FIG. 9 , stressed skin, whenused in semi-monocoque

Still referring to FIG. 9 , aircraft 900 includes a plurality of flightcomponents 904. As used in this disclosure a “flight component” is acomponent that promotes flight and guidance of an aircraft. In anembodiment, flight components 904 may be mechanically coupled to anaircraft. As used herein, a person of ordinary skill in the art wouldunderstand “mechanically coupled” to mean that at least a portion of adevice, component, or circuit is connected to at least a portion of theaircraft via a mechanical coupling. Said mechanical coupling caninclude, for example, rigid coupling, such as beam coupling, bellowscoupling, bushed pin coupling, constant velocity, split-muff coupling,diaphragm coupling, disc coupling, donut coupling, elastic coupling,flexible coupling, fluid coupling, gear coupling, grid coupling, hirthjoints, hydrodynamic coupling, jaw coupling, magnetic coupling, Oldhamcoupling, sleeve coupling, tapered shaft lock, twin spring coupling, ragjoint coupling, universal joints, or any combination thereof. In anembodiment, mechanical coupling may be used to connect the ends ofadjacent parts and/or objects of an electric aircraft. Further, in anembodiment, mechanical coupling may be used to join two pieces ofrotating electric aircraft components.

Still referring to FIG. 9 , power source may include an energy source.An energy source may include, for example, an electrical energy source agenerator, a photovoltaic device, a fuel cell such as a hydrogen fuelcell, direct methanol fuel cell, and/or solid oxide fuel cell, anelectric energy storage device (e.g., a capacitor, an inductor, and/or abattery). An electrical energy source may also include a battery cell,or a plurality of battery cells connected in series into a module andeach module connected in series or in parallel with other modules.Configuration of an energy source containing connected modules may bedesigned to meet an energy or power requirement and may be designed tofit within a designated footprint in an electric aircraft in whichaircraft 900 may be incorporated.

In an embodiment and still referring to FIG. 9 , plurality of flightcomponents 904 may be arranged in a quad copter orientation. As used inthis disclosure a “quad copter orientation” is at least a lift propulsorcomponent oriented in a geometric shape and/or pattern, wherein each ofthe lift propulsor components are located along a vertex of thegeometric shape. For example, and without limitation, a square quadcopter orientation may have four lift propulsor components oriented inthe geometric shape of a square, wherein each of the four lift propulsorcomponents are located along the four vertices of the square shape. As afurther non-limiting example, a hexagonal quad copter orientation mayhave six lift propulsor components oriented in the geometric shape of ahexagon, wherein each of the six lift propulsor components are locatedalong the six vertices of the hexagon shape. In an embodiment, andwithout limitation, quad copter orientation may include a first set oflift propulsor components and a second set of lift propulsor components,wherein the first set of lift propulsor components and the second set oflift propulsor components may include two lift propulsor componentseach, wherein the first set of lift propulsor components and a secondset of lift propulsor components are distinct from one another. Forexample, and without limitation, the first set of lift propulsorcomponents may include two lift propulsor components that rotate in aclockwise direction, wherein the second set of lift propulsor componentsmay include two lift propulsor components that rotate in acounterclockwise direction. In an embodiment, and without limitation,the first set of propulsor lift components may be oriented along a lineoriented 30° from the longitudinal axis of aircraft 900. In anotherembodiment, and without limitation, the second set of propulsor liftcomponents may be oriented along a line oriented 135° from thelongitudinal axis, wherein the first set of lift propulsor componentsline and the second set of lift propulsor components are perpendicularto each other.

Still referring to FIG. 9 , plurality of flight components 904 mayinclude a pusher component 916. As used in this disclosure a “pushercomponent” is a component that pushes and/or thrusts an aircraft througha medium. As a non-limiting example, pusher component 916 may include apusher propeller, a paddle wheel, a pusher motor, a pusher propulsor,and the like. Additionally, or alternatively, pusher flight componentmay include a plurality of pusher flight components. Pusher component916 is configured to produce a forward thrust. As used in thisdisclosure a “forward thrust” is a thrust that forces aircraft through amedium in a horizontal direction, wherein a horizontal direction is adirection parallel to the longitudinal axis. As a non-limiting example,forward thrust may include a force of 1145 N to force aircraft to in ahorizontal direction along the longitudinal axis. As a furthernon-limiting example, forward thrust may include a force of, as anon-limiting example, 300 N to force aircraft 900 in a horizontaldirection along a longitudinal axis. As a further non-limiting example,pusher component 916 may twist and/or rotate to pull air behind it and,at the same time, push aircraft 900 forward with an equal amount offorce. In an embodiment, and without limitation, the more air forcedbehind aircraft, the greater the thrust force with which the aircraft ispushed horizontally will be. In another embodiment, and withoutlimitation, forward thrust may force aircraft 900 through the medium ofrelative air. Additionally or alternatively, plurality of flightcomponents 904 may include one or more puller components. As used inthis disclosure a “puller component” is a component that pulls and/ortows an aircraft through a medium. As a non-limiting example, pullercomponent may include a flight component such as a puller propeller, apuller motor, a tractor propeller, a puller propulsor, and the like.Additionally, or alternatively, puller component may include a pluralityof puller flight components.

In an embodiment and still referring to FIG. 9 , aircraft 900 mayinclude a flight controller located within fuselage 908, wherein aflight controller is described in detail below, in reference to FIG. 9 .In an embodiment, and without limitation, flight controller may beconfigured to operate a fixed-wing flight capability. As used in thisdisclosure a “fixed-wing flight capability” is a method of flightwherein the plurality of laterally extending elements generate lift. Forexample, and without limitation, fixed-wing flight capability maygenerate lift as a function of an airspeed of aircraft 90 and one ormore airfoil shapes of the laterally extending elements, wherein anairfoil is described above in detail. As a further non-limiting example,flight controller may operate the fixed-wing flight capability as afunction of reducing applied torque on lift propulsor component 912. Forexample, and without limitation, flight controller may reduce a torqueof 19 Nm applied to a first set of lift propulsor components to a torqueof 16 Nm. As a further non-limiting example, flight controller mayreduce a torque of 12 Nm applied to a first set of lift propulsorcomponents to a torque of 0 Nm. In an embodiment, and withoutlimitation, flight controller may produce fixed-wing flight capabilityas a function of increasing forward thrust exerted by pusher component916. For example, and without limitation, flight controller may increasea forward thrust of 1000 kN produced by pusher component 916 to aforward thrust of 1100 kN. In an embodiment, and without limitation, anamount of lift generation may be related to an amount of forward thrustgenerated to increase airspeed velocity, wherein the amount of liftgeneration may be directly proportional to the amount of forward thrustproduced. Additionally or alternatively, flight controller may includean inertia compensator. As used in this disclosure an “inertiacompensator” is one or more computing devices, electrical components,logic circuits, processors, and the like there of that are configured tocompensate for inertia in one or more lift propulsor components presentin aircraft 900. Inertia compensator may alternatively or additionallyinclude any computing device used as an inertia compensator as describedin U.S. Nonprovisional application Ser. No. 17/106,557 and entitled“SYSTEM AND METHOD FOR FLIGHT CONTROL IN ELECTRIC AIRCRAFT,” theentirety of which is incorporated herein by reference.

In an embodiment, and still referring to FIG. 9 , flight controller maybe configured to perform a reverse thrust command. As used in thisdisclosure a “reverse thrust command” is a command to perform a thrustthat forces a medium towards the relative air opposing the aircraft. Forexample, reverse thrust command may include a thrust of 180 N directedtowards the nose of aircraft to at least repel and/or oppose therelative air. Reverse thrust command may alternatively or additionallyinclude any reverse thrust command as described in U.S. Nonprovisionalapplication Ser. No. 17/319,155 and entitled “AIRCRAFT HAVING REVERSETHRUST CAPABILITIES,” the entirety of which is incorporated herein byreference. In another embodiment, flight controller may be configured toperform a regenerative drag operation. As used in this disclosure a“regenerative drag operation” is an operating condition of an aircraft,wherein the aircraft has a negative thrust and/or is reducing inairspeed velocity. For example, and without limitation, regenerativedrag operation may include a positive propeller speed and a negativepropeller thrust. Regenerative drag operation may alternatively oradditionally include any regenerative drag operation as described inU.S. Nonprovisional application Ser. No. 17/319,155.

In an embodiment, and still referring to FIG. 9 , flight controller maybe configured to perform a corrective action as a function of a failureevent. As used in this disclosure a “corrective action” is an actionconducted by the plurality of flight components to correct and/or altera movement of an aircraft. For example, and without limitation, acorrective action may include an action to reduce a yaw torque generatedby a failure event. Additionally or alternatively, corrective action mayinclude any corrective action as described in U.S. Nonprovisionalapplication Ser. No. 17/222,539, and entitled “AIRCRAFT FORSELF-NEUTRALIZING FLIGHT,” the entirety of which is incorporated hereinby reference. As used in this disclosure a “failure event” is a failureof a lift propulsor component of the plurality of lift propulsorcomponents. For example, and without limitation, a failure event maydenote a rotation degradation of a rotor, a reduced torque of a rotor,and the like thereof.

Now referring to FIG. 10 , an exemplary embodiment 1000 of a flightcontroller 1004 is illustrated. As used in this disclosure a “flightcontroller” is a computing device of a plurality of computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and flight instruction. Flight controller 1004 may includeand/or communicate with any computing device as described in thisdisclosure, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC) as described in this disclosure. Further, flight controller 1004may include a single computing device operating independently, or mayinclude two or more computing device operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single computing device or in two or more computingdevices. In a non-limiting embodiment, system 100 may include acomputing device wherein the computing device may include flightcontroller 1004 configured to facilitate communication between aplurality of aircrafts and their flight controllers. In embodiments,flight controller 1004 may be installed in an aircraft, may control theaircraft remotely, and/or may include an element installed in theaircraft and a remote element in communication therewith. In someembodiments, flight controller 1004 may be configured to generate a nodeas described in FIG. 1 .

In an embodiment, and still referring to FIG. 10 , flight controller1004 may include a signal transformation component 1008. As used in thisdisclosure a “signal transformation component” is a component thattransforms and/or converts a first signal to a second signal, wherein asignal may include one or more digital and/or analog signals. Forexample, and without limitation, signal transformation component 1008may be configured to perform one or more operations such aspreprocessing, lexical analysis, parsing, semantic analysis, and thelike thereof. In an embodiment, and without limitation, signaltransformation component 1008 may include one or more analog-to-digitalconvertors that transform a first signal of an analog signal to a secondsignal of a digital signal. For example, and without limitation, ananalog-to-digital converter may convert an analog input signal to a10-bit binary digital representation of that signal. In anotherembodiment, signal transformation component 1008 may includetransforming one or more low-level languages such as, but not limitedto, machine languages and/or assembly languages. For example, andwithout limitation, signal transformation component 1008 may includetransforming a binary language signal to an assembly language signal. Inan embodiment, and without limitation, signal transformation component1008 may include transforming one or more high-level languages and/orformal languages such as but not limited to alphabets, strings, and/orlanguages. For example, and without limitation, high-level languages mayinclude one or more system languages, scripting languages,domain-specific languages, visual languages, esoteric languages, and thelike thereof. As a further non-limiting example, high-level languagesmay include one or more algebraic formula languages, business datalanguages, string and list languages, object-oriented languages, and thelike thereof.

Still referring to FIG. 10 , signal transformation component 1008 may beconfigured to optimize an intermediate representation 1012. As used inthis disclosure an “intermediate representation” is a data structureand/or code that represents the input signal. Signal transformationcomponent 1008 may optimize intermediate representation as a function ofa data-flow analysis, dependence analysis, alias analysis, pointeranalysis, escape analysis, and the like thereof. In an embodiment, andwithout limitation, signal transformation component 1008 may optimizeintermediate representation 1012 as a function of one or more inlineexpansions, dead code eliminations, constant propagation, looptransformations, and/or automatic parallelization functions. In anotherembodiment, signal transformation component 1008 may optimizeintermediate representation as a function of a machine dependentoptimization such as a peephole optimization, wherein a peepholeoptimization may rewrite short sequences of code into more efficientsequences of code. Signal transformation component 1008 may optimizeintermediate representation to generate an output language, wherein an“output language,” as used herein, is the native machine language offlight controller 1004. For example, and without limitation, nativemachine language may include one or more binary and/or numericallanguages.

In an embodiment, and still referring to FIG. 10 , flight controller1004 may include a reconfigurable hardware platform 1016. A“reconfigurable hardware platform,” as used herein, is a componentand/or unit of hardware that may be reprogrammed, such that, forinstance, a data path between elements such as logic gates or otherdigital circuit elements may be modified to change an algorithm, state,logical sequence, or the like of the component and/or unit. This may beaccomplished with such flexible high-speed computing fabrics asfield-programmable gate arrays (FPGAs), which may include a grid ofinterconnected logic gates, connections between which may be severedand/or restored to program in modified logic. Reconfigurable hardwareplatform 1016 may be reconfigured to enact any algorithm and/oralgorithm selection process received from another computing deviceand/or created using machine-learning processes.

Still referring to FIG. 10 , reconfigurable hardware platform 1016 mayinclude a logic component 1020. As used in this disclosure a “logiccomponent” is a component that executes instructions on output language.For example, and without limitation, logic component may perform basicarithmetic, logic, controlling, input/output operations, and the likethereof. Logic component 1020 may include any suitable processor, suchas without limitation a component incorporating logical circuitry forperforming arithmetic and logical operations, such as an arithmetic andlogic unit (ALU), which may be regulated with a state machine anddirected by operational inputs from memory and/or sensors; logiccomponent 1020 may be organized according to Von Neumann and/or Harvardarchitecture as a non-limiting example. Logic component 1020 mayinclude, incorporate, and/or be incorporated in, without limitation, amicrocontroller, microprocessor, digital signal processor (DSP), FieldProgrammable Gate Array (FPGA), Complex Programmable Logic Device(CPLD), Graphical Processing Unit (GPU), general purpose GPU, TensorProcessing Unit (TPU), analog or mixed signal processor, TrustedPlatform Module (TPM), a floating point unit (FPU), and/or system on achip (SoC). In an embodiment, logic component 1020 may include one ormore integrated circuit microprocessors, which may contain one or morecentral processing units, central processors, and/or main processors, ona single metal-oxide-semiconductor chip. Logic component 1020 may beconfigured to execute a sequence of stored instructions to be performedon the output language and/or intermediate representation 1012. Logiccomponent 1020 may be configured to fetch and/or retrieve theinstruction from a memory cache, wherein a “memory cache,” as used inthis disclosure, is a stored instruction set on flight controller 1004.Logic component 1020 may be configured to decode the instructionretrieved from the memory cache to opcodes and/or operands. Logiccomponent 1020 may be configured to execute the instruction onintermediate representation 1012 and/or output language. For example,and without limitation, logic component 1020 may be configured toexecute an addition operation on intermediate representation 1012 and/oroutput language.

In an embodiment, and without limitation, logic component 1020 may beconfigured to calculate a flight element 1024. As used in thisdisclosure a “flight element” is an element of datum denoting a relativestatus of aircraft. For example, and without limitation, flight element1024 may denote one or more torques, thrusts, airspeed velocities,forces, altitudes, groundspeed velocities, directions during flight,directions facing, forces, orientations, and the like thereof. Forexample, and without limitation, flight element 1024 may denote thataircraft is cruising at an altitude and/or with a sufficient magnitudeof forward thrust. As a further non-limiting example, flight status maydenote that is building thrust and/or groundspeed velocity inpreparation for a takeoff. As a further non-limiting example, flightelement 1024 may denote that aircraft is following a flight pathaccurately and/or sufficiently.

In an embodiment, and still referring to FIG. 10 , flight controller1004 may be configured generate an autonomous function. As used in thisdisclosure an “autonomous function” is a mode and/or function of flightcontroller 1004 that controls aircraft automatically. For example, andwithout limitation, autonomous function may perform one or more aircraftmaneuvers, take offs, landings, altitude adjustments, flight levelingadjustments, turns, climbs, and/or descents. As a further non-limitingexample, autonomous function may adjust one or more airspeed velocities,thrusts, torques, and/or groundspeed velocities. As a furthernon-limiting example, autonomous function may perform one or more flightpath corrections and/or flight path modifications as a function offlight element 1024. In an embodiment, autonomous function may includeone or more modes of autonomy such as, but not limited to, autonomousmode, semi-autonomous mode, and/or non-autonomous mode. As used in thisdisclosure “autonomous mode” is a mode that automatically adjusts and/orcontrols aircraft and/or the maneuvers of aircraft in its entirety. Forexample, autonomous mode may denote that flight controller 1004 willadjust the aircraft. As used in this disclosure a “semi-autonomous mode”is a mode that automatically adjusts and/or controls a portion and/orsection of aircraft. For example, and without limitation,semi-autonomous mode may denote that a pilot will control thepropulsors, wherein flight controller 1004 will control the aileronsand/or rudders. As used in this disclosure “non-autonomous mode” is amode that denotes a pilot will control aircraft and/or maneuvers ofaircraft in its entirety.

In an embodiment, and still referring to FIG. 10 , flight controller1004 may generate autonomous function as a function of an autonomousmachine-learning model. As used in this disclosure an “autonomousmachine-learning model” is a machine-learning model to produce anautonomous function output given flight element 1024 and a pilot signal1036 as inputs; this is in contrast to a non-machine learning softwareprogram where the commands to be executed are determined in advance by auser and written in a programming language. As used in this disclosure a“pilot signal” is an element of datum representing one or more functionsa pilot is controlling and/or adjusting. For example, pilot signal 1036may denote that a pilot is controlling and/or maneuvering ailerons,wherein the pilot is not in control of the rudders and/or propulsors. Inan embodiment, pilot signal 1036 may include an implicit signal and/oran explicit signal. For example, and without limitation, pilot signal1036 may include an explicit signal, wherein the pilot explicitly statesthere is a lack of control and/or desire for autonomous function. As afurther non-limiting example, pilot signal 1036 may include an explicitsignal directing flight controller 1004 to control and/or maintain aportion of aircraft, a portion of the flight plan, the entire aircraft,and/or the entire flight plan. As a further non-limiting example, pilotsignal 1036 may include an implicit signal, wherein flight controller1004 detects a lack of control such as by a malfunction, torquealteration, flight path deviation, and the like thereof. In anembodiment, and without limitation, pilot signal 1036 may include one ormore explicit signals to reduce torque, and/or one or more implicitsignals that torque may be reduced due to reduction of airspeedvelocity. In an embodiment, and without limitation, pilot signal 1036may include one or more local and/or global signals. For example, andwithout limitation, pilot signal 1036 may include a local signal that istransmitted by a pilot and/or crew member. As a further non-limitingexample, pilot signal 1036 may include a global signal that istransmitted by air traffic control and/or one or more remote users thatare in communication with the pilot of aircraft. In an embodiment, pilotsignal 1036 may be received as a function of a tri-state bus and/ormultiplexor that denotes an explicit pilot signal should be transmittedprior to any implicit or global pilot signal.

Still referring to FIG. 10 , autonomous machine-learning model mayinclude one or more autonomous machine-learning processes such assupervised, unsupervised, or reinforcement machine-learning processesthat flight controller 1004 and/or a remote device may or may not use inthe generation of autonomous function. As used in this disclosure“remote device” is an external device to flight controller 1004.Additionally or alternatively, autonomous machine-learning model mayinclude one or more autonomous machine-learning processes that afield-programmable gate array (FPGA) may or may not use in thegeneration of autonomous function. Autonomous machine-learning processmay include, without limitation machine learning processes such assimple linear regression, multiple linear regression, polynomialregression, support vector regression, ridge regression, lassoregression, elasticnet regression, decision tree regression, randomforest regression, logistic regression, logistic classification,K-nearest neighbors, support vector machines, kernel support vectormachines, naïve bayes, decision tree classification, random forestclassification, K-means clustering, hierarchical clustering,dimensionality reduction, principal component analysis, lineardiscriminant analysis, kernel principal component analysis, Q-learning,State Action Reward State Action (SARSA), Deep-Q network, Markovdecision processes, Deep Deterministic Policy Gradient (DDPG), or thelike thereof.

In an embodiment, and still referring to FIG. 10 , autonomous machinelearning model may be trained as a function of autonomous training data,wherein autonomous training data may correlate a flight element, pilotsignal, and/or simulation data to an autonomous function. For example,and without limitation, a flight element of an airspeed velocity, apilot signal of limited and/or no control of propulsors, and asimulation data of required airspeed velocity to reach the destinationmay result in an autonomous function that includes a semi-autonomousmode to increase thrust of the propulsors. Autonomous training data maybe received as a function of user-entered valuations of flight elements,pilot signals, simulation data, and/or autonomous functions. Flightcontroller 1004 may receive autonomous training data by receivingcorrelations of flight element, pilot signal, and/or simulation data toan autonomous function that were previously received and/or determinedduring a previous iteration of generation of autonomous function.Autonomous training data may be received by one or more remote devicesand/or FPGAs that at least correlate a flight element, pilot signal,and/or simulation data to an autonomous function. Autonomous trainingdata may be received in the form of one or more user-enteredcorrelations of a flight element, pilot signal, and/or simulation datato an autonomous function.

Still referring to FIG. 10 , flight controller 1004 may receiveautonomous machine-learning model from a remote device and/or FPGA thatutilizes one or more autonomous machine learning processes, wherein aremote device and an FPGA is described above in detail. For example, andwithout limitation, a remote device may include a computing device,external device, processor, FPGA, microprocessor and the like thereof.Remote device and/or FPGA may perform the autonomous machine-learningprocess using autonomous training data to generate autonomous functionand transmit the output to flight controller 1004. Remote device and/orFPGA may transmit a signal, bit, datum, or parameter to flightcontroller 1004 that at least relates to autonomous function.Additionally or alternatively, the remote device and/or FPGA may providean updated machine-learning model. For example, and without limitation,an updated machine-learning model may be comprised of a firmware update,a software update, an autonomous machine-learning process correction,and the like thereof. As a non-limiting example a software update mayincorporate a new simulation data that relates to a modified flightelement. Additionally or alternatively, the updated machine learningmodel may be transmitted to the remote device and/or FPGA, wherein theremote device and/or FPGA may replace the autonomous machine-learningmodel with the updated machine-learning model and generate theautonomous function as a function of the flight element, pilot signal,and/or simulation data using the updated machine-learning model. Theupdated machine-learning model may be transmitted by the remote deviceand/or FPGA and received by flight controller 1004 as a software update,firmware update, or corrected autonomous machine-learning model. Forexample, and without limitation autonomous machine learning model mayutilize a neural net machine-learning process, wherein the updatedmachine-learning model may incorporate a gradient boostingmachine-learning process.

Still referring to FIG. 10 , flight controller 1004 may include, beincluded in, and/or communicate with a mobile device such as a mobiletelephone or smartphone. Further, flight controller may communicate withone or more additional devices as described below in further detail viaa network interface device. The network interface device may be utilizedfor commutatively connecting a flight controller to one or more of avariety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. The network may include anynetwork topology and can may employ a wired and/or a wireless mode ofcommunication.

In an embodiment, and still referring to FIG. 10 , flight controller1004 may include, but is not limited to, for example, a cluster offlight controllers in a first location and a second flight controller orcluster of flight controllers in a second location. Flight controller1004 may include one or more flight controllers dedicated to datastorage, security, distribution of traffic for load balancing, and thelike. Flight controller 1004 may be configured to distribute one or morecomputing tasks as described below across a plurality of flightcontrollers, which may operate in parallel, in series, redundantly, orin any other manner used for distribution of tasks or memory betweencomputing devices. For example, and without limitation, flightcontroller 1004 may implement a control algorithm to distribute and/orcommand the plurality of flight controllers. As used in this disclosurea “control algorithm” is a finite sequence of well-defined computerimplementable instructions that may determine the flight component ofthe plurality of flight components to be adjusted. For example, andwithout limitation, control algorithm may include one or more algorithmsthat reduce and/or prevent aviation asymmetry. As a further non-limitingexample, control algorithms may include one or more models generated asa function of a software including, but not limited to Simulink byMathWorks, Natick, Mass., USA. In an embodiment, and without limitation,control algorithm may be configured to generate an auto-code, wherein an“auto-code,” is used herein, is a code and/or algorithm that isgenerated as a function of the one or more models and/or software's. Inanother embodiment, control algorithm may be configured to produce asegmented control algorithm. As used in this disclosure a “segmentedcontrol algorithm” is control algorithm that has been separated and/orparsed into discrete sections. For example, and without limitation,segmented control algorithm may parse control algorithm into two or moresegments, wherein each segment of control algorithm may be performed byone or more flight controllers operating on distinct flight components.

Still referring to FIG. 10 , a node may include, without limitation aplurality of inputs x_(i) that may receive numerical values from inputsto a neural network containing the node and/or from other nodes. Nodemay perform a weighted sum of inputs using weights w_(i) that aremultiplied by respective inputs x_(i). Additionally or alternatively, abias b may be added to the weighted sum of the inputs such that anoffset is added to each unit in the neural network layer that isindependent of the input to the layer. The weighted sum may then beinput into a function φ, which may generate one or more outputs y.Weight w_(i) applied to an input x_(i) may indicate whether the input is“excitatory,” indicating that it has strong influence on the one or moreoutputs y, for instance by the corresponding weight having a largenumerical value, and/or a “inhibitory,” indicating it has a weak effectinfluence on the one more inputs y, for instance by the correspondingweight having a small numerical value. The values of weights w_(i) maybe determined by training a neural network using training data, whichmay be performed using any suitable process as described above. In anembodiment, and without limitation, a neural network may receivesemantic units as inputs and output vectors representing such semanticunits according to weights w_(i) that are derived using machine-learningprocesses as described in this disclosure.

Still referring to FIG. 10 , flight controller may include asub-controller 1040. As used in this disclosure a “sub-controller” is acontroller and/or component that is part of a distributed controller asdescribed above; for instance, flight controller 1004 may be and/orinclude a distributed flight controller made up of one or moresub-controllers. For example, and without limitation, sub-controller1040 may include any controllers and/or components thereof that aresimilar to distributed flight controller and/or flight controller asdescribed above. Sub-controller 1040 may include any component of anyflight controller as described above. Sub-controller 1040 may beimplemented in any manner suitable for implementation of a flightcontroller as described above. As a further non-limiting example,sub-controller 1040 may include one or more processors, logic componentsand/or computing devices capable of receiving, processing, and/ortransmitting data across the distributed flight controller as describedabove. As a further non-limiting example, sub-controller 1040 mayinclude a controller that receives a signal from a first flightcontroller and/or first distributed flight controller component andtransmits the signal to a plurality of additional sub-controllers and/orflight components.

Still referring to FIG. 10 , flight controller may include aco-controller 1044. As used in this disclosure a “co-controller” is acontroller and/or component that joins flight controller 1004 ascomponents and/or nodes of a distributer flight controller as describedabove. For example, and without limitation, co-controller 1044 mayinclude one or more controllers and/or components that are similar toflight controller 1004. As a further non-limiting example, co-controller1044 may include any controller and/or component that joins flightcontroller 1004 to distributer flight controller. As a furthernon-limiting example, co-controller 1044 may include one or moreprocessors, logic components and/or computing devices capable ofreceiving, processing, and/or transmitting data to and/or from flightcontroller 1004 to distributed flight control system. Co-controller 1044may include any component of any flight controller as described above.Co-controller 1044 may be implemented in any manner suitable forimplementation of a flight controller as described above.

In an embodiment, and with continued reference to FIG. 10 , flightcontroller 1004 may be designed and/or configured to perform any method,method step, or sequence of method steps in any embodiment described inthis disclosure, in any order and with any degree of repetition. Forinstance, flight controller 1004 may be configured to perform a singlestep or sequence repeatedly until a desired or commanded outcome isachieved; repetition of a step or a sequence of steps may be performediteratively and/or recursively using outputs of previous repetitions asinputs to subsequent repetitions, aggregating inputs and/or outputs ofrepetitions to produce an aggregate result, reduction or decrement ofone or more variables such as global variables, and/or division of alarger processing task into a set of iteratively addressed smallerprocessing tasks. Flight controller may perform any step or sequence ofsteps as described in this disclosure in parallel, such assimultaneously and/or substantially simultaneously performing a step twoor more times using two or more parallel threads, processor cores, orthe like; division of tasks between parallel threads and/or processesmay be performed according to any protocol suitable for division oftasks between iterations. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in whichsteps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing

Referring now to FIG. 11 , an exemplary embodiment of a machine-learningmodule 1100 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 1104 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 1108 given data provided as inputs1112; this is in contrast to a non-machine learning software programwhere the commands to be executed are determined in advance by a userand written in a programming language.

Still referring to FIG. 11 , “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 1104 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 1104 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 1104 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 1104 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 1104 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 1104 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data1104 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 11 ,training data 1104 may include one or more elements that are notcategorized; that is, training data 1104 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 1104 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 1104 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 1104 used by machine-learning module 1100 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure.

Further referring to FIG. 11 , training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 1116. Training data classifier 1116 may include a“classifier,” which as used in this disclosure is a machine-learningmodel as defined below, such as a mathematical model, neural net, orprogram generated by a machine learning algorithm known as a“classification algorithm,” as described in further detail below, thatsorts inputs into categories or bins of data, outputting the categoriesor bins of data and/or labels associated therewith. A classifier may beconfigured to output at least a datum that labels or otherwiseidentifies a set of data that are clustered together, found to be closeunder a distance metric as described below, or the like.Machine-learning module 1100 may generate a classifier using aclassification algorithm, defined as a processes whereby a computingdevice and/or any module and/or component operating thereon derives aclassifier from training data 1104. Classification may be performedusing, without limitation, linear classifiers such as without limitationlogistic regression and/or naive Bayes classifiers, nearest neighborclassifiers such as k-nearest neighbors classifiers, support vectormachines, least squares support vector machines, fisher's lineardiscriminant, quadratic classifiers, decision trees, boosted trees,random forest classifiers, learning vector quantization, and/or neuralnetwork-based classifiers. As a non-limiting example, training dataclassifier 1116 may classify elements of training data to datatransmission pathways.

Still referring to FIG. 11 , machine-learning module 1100 may beconfigured to perform a lazy-learning process 1120 and/or protocol,which may alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 1104.Heuristic may include selecting some number of highest-rankingassociations and/or training data 1104 elements. Lazy learning mayimplement any suitable lazy learning algorithm, including withoutlimitation a K-nearest neighbors algorithm, a lazy naïve Bayesalgorithm, or the like; persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various lazy-learningalgorithms that may be applied to generate outputs as described in thisdisclosure, including without limitation lazy learning applications ofmachine-learning algorithms as described in further detail below.

Alternatively or additionally, and with continued reference to FIG. 11 ,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 1124. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above, and stored in memory; aninput is submitted to a machine-learning model 1124 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 1124 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 1104set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 11 , machine-learning algorithms may include atleast a supervised machine-learning process 1128. At least a supervisedmachine-learning process 1128, as defined herein, include algorithmsthat receive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude node connections as described above as inputs, data transmissionpathways as outputs, and a scoring function representing a desired formof relationship to be detected between inputs and outputs; scoringfunction may, for instance, seek to maximize the probability that agiven input and/or combination of elements inputs is associated with agiven output to minimize the probability that a given input is notassociated with a given output. Scoring function may be expressed as arisk function representing an “expected loss” of an algorithm relatinginputs to outputs, where loss is computed as an error functionrepresenting a degree to which a prediction generated by the relation isincorrect when compared to a given input-output pair provided intraining data 1104. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various possiblevariations of at least a supervised machine-learning process 1128 thatmay be used to determine relation between inputs and outputs. Supervisedmachine-learning processes may include classification algorithms asdefined above.

Further referring to FIG. 11 , machine learning processes may include atleast an unsupervised machine-learning processes 1132. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 11 , machine-learning module 1100 may bedesigned and configured to create a machine-learning model 1124 usingtechniques for development of linear regression models. Linearregression models may include ordinary least squares regression, whichaims to minimize the square of the difference between predicted outcomesand actual outcomes according to an appropriate norm for measuring sucha difference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 11 , machine-learning algorithms mayinclude, without limitation, linear discriminant analysis.Machine-learning algorithm may include quadratic discriminate analysis.Machine-learning algorithms may include kernel ridge regression.Machine-learning algorithms may include support vector machines,including without limitation support vector classification-basedregression processes. Machine-learning algorithms may include stochasticgradient descent algorithms, including classification and regressionalgorithms based on stochastic gradient descent. Machine-learningalgorithms may include nearest neighbors algorithms. Machine-learningalgorithms may include various forms of latent space regularization suchas variational regularization. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 12 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 1200 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 1200 includes a processor 1204 and a memory1208 that communicate with each other, and with other components, via abus 1212. Bus 1212 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 1204 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 1204 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 1204 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC).

Memory 1208 may include various components (e.g., machine-readablemedia) including, but not limited to, a random-access memory component,a read only component, and any combinations thereof. In one example, abasic input/output system 1216 (BIOS), including basic routines thathelp to transfer information between elements within computer system1200, such as during start-up, may be stored in memory 1208. Memory 1208may also include (e.g., stored on one or more machine-readable media)instructions (e.g., software) 1220 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 1208 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 1200 may also include a storage device 1224. Examples ofa storage device (e.g., storage device 1224) include, but are notlimited to, a hard disk drive, a magnetic disk drive, an optical discdrive in combination with an optical medium, a solid-state memorydevice, and any combinations thereof. Storage device 1224 may beconnected to bus 1212 by an appropriate interface (not shown). Exampleinterfaces include, but are not limited to, SCSI, advanced technologyattachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394(FIREWIRE), and any combinations thereof. In one example, storage device1224 (or one or more components thereof) may be removably interfacedwith computer system 1200 (e.g., via an external port connector (notshown)). Particularly, storage device 1224 and an associatedmachine-readable medium 1228 may provide nonvolatile and/or volatilestorage of machine-readable instructions, data structures, programmodules, and/or other data for computer system 1200. In one example,software 1220 may reside, completely or partially, withinmachine-readable medium 1228. In another example, software 1220 mayreside, completely or partially, within processor 1204.

Computer system 1200 may also include an input device 1232. In oneexample, a user of computer system 1200 may enter commands and/or otherinformation into computer system 1200 via input device 1232. Examples ofan input device 1232 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 1232may be interfaced to bus 1212 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 1212, and any combinations thereof. Input device 1232may include a touch screen interface that may be a part of or separatefrom display 1236, discussed further below. Input device 1232 may beutilized as a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 1200 via storage device 1224 (e.g., a removable disk drive, aflash drive, etc.) and/or network interface device 1240. A networkinterface device, such as network interface device 1240, may be utilizedfor connecting computer system 1200 to one or more of a variety ofnetworks, such as network 1244, and one or more remote devices 1248connected thereto. Examples of a network interface device include, butare not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A network,such as network 1244, may employ a wired and/or a wireless mode ofcommunication. In general, any network topology may be used. Information(e.g., data, software 1220, etc.) may be communicated to and/or fromcomputer system 1200 via network interface device 1240.

Computer system 1200 may further include a video display adapter 1252for communicating a displayable image to a display device, such asdisplay device 1236. Examples of a display device include, but are notlimited to, a liquid crystal display (LCD), a cathode ray tube (CRT), aplasma display, a light emitting diode (LED) display, and anycombinations thereof. Display adapter 1252 and display device 1236 maybe utilized in combination with processor 1204 to provide graphicalrepresentations of aspects of the present disclosure. In addition to adisplay device, computer system 1200 may include one or more otherperipheral output devices including, but not limited to, an audiospeaker, a printer, and any combinations thereof. Such peripheral outputdevices may be connected to bus 1212 via a peripheral interface 1256.Examples of a peripheral interface include, but are not limited to, aserial port, a USB connection, a FIREWIRE connection, a parallelconnection, and any combinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. An apparatus for swarm communication for anelectric aircraft fleet, the system comprising: a computing devicecommunicatively connected to a mesh network configured to connect aplurality of electric aircraft to each other, wherein the computingdevice comprises: an authentication module, wherein the authenticationmodule is configured to authenticate each electric aircraft; at least anode, wherein the at least a node comprises at least a communicationcomponent, wherein the at least a communication component is configuredto: receive a plurality of aircraft data, wherein aircraft datacomprises component state data; and facilitate communication between theplurality of electric aircraft as a function of the communicationsatisfying a communication threshold, wherein the communicationthreshold comprises an error rate; and a cloud database, wherein thecloud database is configured to record the plurality of aircraft data.2. The apparatus of claim 1, wherein the mesh network comprises: aplurality of local mesh networks, wherein each local network comprises aplurality of nodes representing non-local entities; and a central meshnetwork, wherein the central mesh network is configured to communicatewith the plurality of nodes representing non-local entities as afunction of the plurality of local mesh networks.
 3. The apparatus ofclaim 2, wherein the central mesh network comprises a computing deviceconfigured to temporarily merge the plurality of local mesh networkswith the central mesh network, wherein merging the plurality of localmesh networks comprises generating additional nodes for each non-localentity.
 4. The apparatus of claim 3, wherein the central mesh network isfurther configured to delete the additional nodes once communicationwith the non-local entities are complete.
 5. The apparatus of claim 1,wherein the computing device is further configured to assign acommunication component of the plurality of communication components toan electric aircraft of the plurality of the electric aircrafts.
 6. Theapparatus of claim 5, wherein computing device is further configured to:receive, as a function of a communication component, an aircraft datafrom its assigned electric aircraft; and transmit the aircraft data to asecond electric aircraft as a function of the communication componentassigned to the second electric aircraft.
 7. The apparatus of claim 1,wherein the authentication module is further configured to: receive acredential from an electric aircraft of the plurality of electricaircrafts; compare the credential to an authorized credential storedwithin an authentication database; and bypass authentication for theelectric aircraft as a function of the comparison.
 8. The apparatus ofclaim 1, wherein the computing device further comprises a user device,wherein the user device is configured to manually facilitatecommunication between the plurality of electric aircrafts.
 9. Theapparatus of claim 8, wherein the computing device is further configuredto: generate an aircraft data model as a function of the aircraft data;and display the aircraft data model onto the user device.
 10. Theapparatus of claim 1, wherein the electric aircraft fleet furthercomprises a manned electric vertical take-off and landing aircraft. 11.A method for swarm communication for an electric aircraft fleet, themethod comprising: connecting, as a function of a mesh network, aplurality of electric aircrafts with each other; authenticating, by anauthentication module of a computing device, each electric aircraft;receiving, by at least a node comprising a communication component of aplurality of communication components, a plurality of aircraft data,wherein aircraft data comprises component state data; facilitating, bythe communication component, communication between the plurality ofelectric aircrafts as a function of the communication satisfying acommunication threshold, wherein the communication threshold comprisesan error rate; and recording, by a cloud database, the plurality ofaircraft data.
 12. The method of claim 11, wherein the mesh networkcomprises: a plurality of local mesh networks, wherein each localnetwork comprises a plurality of nodes representing non-local entities;and a central mesh network, wherein the central mesh network isconfigured to communicate with the plurality of nodes representingnon-local entities as a function of the plurality of local meshnetworks.
 13. The method of claim 12, wherein the method furthercomprises: temporarily merging the plurality of local mesh networks withthe central mesh network, wherein merging the plurality of local meshnetworks comprises generating additional nodes for each non-localentity.
 14. The method of claim 13, wherein the method further comprisesdeleting the additional nodes once communication with the non-localentities are complete.
 15. The method of claim 11, wherein the methodfurther comprises assigning, by the computing device, a communicationcomponent of the plurality of communication components to an electricaircraft of the plurality of the electric aircrafts.
 16. The method ofclaim 15, wherein the method further comprises: receiving, by thecomputing device, an aircraft data from its assigned electric aircraftas a function of a communication component; and transmitting theaircraft data to a second electric aircraft as a function of thecommunication component assigned to the second electric aircraft. 17.The method of claim 11, wherein the method further comprises: receiving,by the authentication module, a credential from an electric aircraft ofthe plurality of electric aircrafts; comparing the credential to anauthorized credential stored within an authentication database; andbypassing authentication for the electric aircraft as a function of thecomparison.
 18. The method of claim 11, wherein the method furthercomprises: manually facilitating, by a user device of the computingdevice, communication between the plurality of electric aircrafts. 19.The method of claim 18, wherein manually facilitating further comprises:generating, by the computing device, an aircraft data model as afunction of the aircraft data; and displaying, by the user device, theaircraft data model onto the user device.
 20. The method of claim 11,wherein the electric aircraft fleet further comprises a manned electricvertical take-off and landing aircraft.